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A combination of the results of several searches for the electroweak production of the supersymmetric partners of standard model bosons, and of charged leptons, is presented. All searches use proton-proton collision data at $\sqrt{s}$ = 13 TeV recorded with the CMS detector at the LHC in 2016-2018. The analyzed data correspond to an integrated luminosity of up to 137 fb$^{-1}$. The results are interpreted in terms of simplified models of supersymmetry. Two new interpretations are added with this combination: a model spectrum with the bino as the lightest supersymmetric particle together with mass-degenerate higgsinos decaying to the bino and a standard model boson, and the compressed-spectrum region of a previously studied model of slepton pair production. Improved analysis techniques are employed to optimize sensitivity for the compressed spectra in the wino and slepton pair production models. The results are consistent with expectations from the standard model. The combination provides a more comprehensive coverage of the model parameter space than the individual searches, extending the exclusion by up to 125 GeV, and also targets some of the intermediate gaps in the mass coverage.
A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb$^{-1}$ of proton$-$proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple signal regions are defined, targeting several SUSY simplified models yielding the desired final states. A single control region is used to constrain the normalisation of the $WZ$+jets background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of several supersymmetric models featuring R-parity conservation or R-parity violation, yielding exclusion limits surpassing those from previous searches. In models considering gluino (squark) pair production, gluino (squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level.
Observed exclusion limits at 95% CL from Fig 7(a) for $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Positive one $\sigma$ observed exclusion limits at 95% CL from Fig 7(a) for $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Negative one $\sigma$ observed exclusion limits at 95% CL from Fig 7(a) for $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Expected exclusion limits at 95% CL from Fig 7(a) for $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
One $\sigma$ band of expected exclusion limits at 95% CL from Fig 7(a) for $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Observed exclusion limits at 95% CL from Fig 7(c) for $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Positive one $\sigma$ observed exclusion limits at 95% CL from Fig 7(c) for $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Negative one $\sigma$ observed exclusion limits at 95% CL from Fig 7(c) for $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Expected exclusion limits at 95% CL from Fig 7(c) for $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
One $\sigma$ band of expected exclusion limits at 95% CL from Fig 7(c) for $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Observed exclusion limits at 95% CL from Fig 7(f) for $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Positive one $\sigma$ observed exclusion limits at 95% CL from Fig 7(f) for $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Negative one $\sigma$ observed exclusion limits at 95% CL from Fig 7(f) for $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Expected exclusion limits at 95% CL from Fig 7(f) for $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
One $\sigma$ band of expected exclusion limits at 95% CL from Fig 7(f) for $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Observed exclusion limits at 95% CL from Fig 7(e) for direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$
Positive one $\sigma$ observed exclusion limits at 95% CL from Fig 7(e) for direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$
Negative one $\sigma$ observed exclusion limits at 95% CL from Fig 7(e) for direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$
Expected exclusion limits at 95% CL from Fig 7(e) for direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$
One $\sigma$ band of expected exclusion limits at 95% CL from Fig 7(e) for direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$
Observed exclusion limits at 95% CL from Fig 7(b) for $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Positive one $\sigma$ observed exclusion limits at 95% CL from Fig 7(b) for $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Negative one $\sigma$ observed exclusion limits at 95% CL from Fig 7(b) for $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Expected exclusion limits at 95% CL from Fig 7(b) for $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
One $\sigma$ band of expected exclusion limits at 95% CL from Fig 7(b) for $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Observed exclusion limits at 95% CL from Fig 7(d) for $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Positive one $\sigma$ observed exclusion limits at 95% CL from Fig 7(d) for $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Negative one $\sigma$ observed exclusion limits at 95% CL from Fig 7(d) for $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Expected exclusion limits at 95% CL from Fig 7(d) for $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
One $\sigma$ band of expected exclusion limits at 95% CL from Fig 7(d) for $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
N-1 distribution for $m_{\mathrm{eff}}$of observed data and expected background in SRGGWZ-H.
N-1 distribution for $E_{\mathrm{T}}^{\mathrm{miss}}$of observed data and expected background in SRGGSlep-M.
N-1 distribution for $\sum{p_{\mathrm{T}}^\mathrm{jet}}$of observed data and expected background in SRUDD-ge2b.
N-1 distribution for $m_{\mathrm{eff}}$of observed data and expected background in SRLQD.
N-1 distribution for $m_{\mathrm{eff}}$of observed data and expected background in SRSSWZ-H.
N-1 distribution for $m_{\mathrm{eff}}$of observed data and expected background in SRSSSlep-H(loose).
Signal acceptance for SRGGWZ-H signal region from Fig 10(c) in a SUSY scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRGGWZ-H signal region from Fig 15(c) in a SUSY scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRGGWZ-M signal region from Fig 10(b) in a SUSY scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRGGWZ-M signal region from Fig 15(b) in a SUSY scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRGGWZ-L signal region from Fig 10(a) in a SUSY scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRGGWZ-L signal region from Fig 15(a) in a SUSY scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRGGSlep-L signal region from Fig 12(a) in a SUSY scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRGGSlep-L signal region from Fig 17(a) in a SUSY scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRGGSlep-M signal region from Fig 12(b) in a SUSY scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRGGSlep-M signal region from Fig 17(b) in a SUSY scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRGGSlep-H signal region from Fig 12(c) in a SUSY scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRGGSlep-H signal region from Fig 17(c) in a SUSY scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRUDD-1b signal region from Fig 14(b) in a SUSY scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Signal efficiency for SRUDD-1b signal region from Fig 19(b) in a SUSY scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Signal acceptance for SRUDD-2b signal region from Fig 14(c) in a SUSY scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Signal efficiency for SRUDD-2b signal region from Fig 19(c) in a SUSY scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Signal acceptance for SRUDD-ge2b signal region from Fig 14(d) in a SUSY scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Signal efficiency for SRUDD-ge2b signal region from Fig 19(d) in a SUSY scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Signal acceptance for SRUDD-ge3b signal region from Fig 14(e) in a SUSY scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Signal efficiency for SRUDD-ge3b signal region from Fig 19(e) in a SUSY scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Signal acceptance for SRLQD signal region from Fig 14(a) in a SUSY scenario where direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$
Signal efficiency for SRLQD signal region from Fig 19(a) in a SUSY scenario where direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$
Signal acceptance for SRSSWZ-L signal region from Fig 11(a) in a SUSY scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSWZ-L signal region from Fig 16(a) in a SUSY scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRSSWZ-ML signal region from Fig 11(b) in a SUSY scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSWZ-ML signal region from Fig 16(b) in a SUSY scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRSSWZ-MH signal region from Fig 11(c) in a SUSY scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSWZ-MH signal region from Fig 16(c) in a SUSY scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRSSWZ-H signal region from Fig 11(d) in a SUSY scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSWZ-H signal region from Fig 16(d) in a SUSY scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRSSSlep-H signal region from Fig 13(d) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSSlep-H signal region from Fig 18(d) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRSSSlep-MH signal region from Fig 13(c) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSSlep-MH signal region from Fig 18(c) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRSSSlep-L signal region from Fig 13(a) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSSlep-L signal region from Fig 18(a) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRSSSlep-ML signal region from Fig 13(b) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSSlep-ML signal region from Fig 18(b) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal acceptance for SRSSSlep-H(loose) signal region from Fig 13(e) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Signal efficiency for SRSSSlep-H(loose) signal region from Fig 18(e) in a SUSY scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRGGWZ-H in a susy scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 1400 GeV, $m(\tilde{\chi_{1}^{0}})$ = 1000 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRGGWZ-M in a susy scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 1400 GeV, $m(\tilde{\chi_{1}^{0}})$ = 1000 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRGGWZ-L in a susy scenario where $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 1400 GeV, $m(\tilde{\chi_{1}^{0}})$ = 1000 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRGGSlep-L in a susy scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 2000 GeV, $m(\tilde{\chi_{1}^{0}})$ = 500 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRGGSlep-M in a susy scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 2000 GeV, $m(\tilde{\chi_{1}^{0}})$ = 500 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRGGSlep-H in a susy scenario where $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 2000 GeV, $m(\tilde{\chi_{1}^{0}})$ = 500 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRUDD-1b in a susy scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 1600 GeV, $m(\tilde{t})$ = 600 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRUDD-2b in a susy scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 1600 GeV, $m(\tilde{t})$ = 600 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRUDD-ge2b in a susy scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 1600 GeV, $m(\tilde{t})$ = 600 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRUDD-ge3b in a susy scenario where $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 1600 GeV, $m(\tilde{t})$ = 600 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRLQD in a susy scenario where direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$. The masses of the superpartners involved in the process are set to $m(\tilde{g})$ = 2200 GeV, $m(\tilde{\chi_{1}^{0}})$ = 1870 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSWZ-L in a susy scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 800 GeV, $m(\tilde{\chi_{1}^{0}})$ = 600 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSWZ-ML in a susy scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 800 GeV, $m(\tilde{\chi_{1}^{0}})$ = 600 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSWZ-MH in a susy scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 800 GeV, $m(\tilde{\chi_{1}^{0}})$ = 600 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSWZ-H in a susy scenario where $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 800 GeV, $m(\tilde{\chi_{1}^{0}})$ = 600 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSSlep-H in a susy scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 1000 GeV, $m(\tilde{\chi_{1}^{0}})$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSSlep-MH in a susy scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 1000 GeV, $m(\tilde{\chi_{1}^{0}})$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSSlep-L in a susy scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 1000 GeV, $m(\tilde{\chi_{1}^{0}})$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSSlep-ML in a susy scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 1000 GeV, $m(\tilde{\chi_{1}^{0}})$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region SRSSSlep-H(loose) in a susy scenario where $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$. The masses of the superpartners involved in the process are set to $m(\tilde{q})$ = 1000 GeV, $m(\tilde{\chi_{1}^{0}})$ = 800 GeV. Only statistical uncertainties are shown.
Cross-section upper limits at 95% CL from Fig1(a) for $\tilde{g}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Cross-section upper limits at 95% CL from Fig1(c) for $\tilde{g}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
Cross-section upper limits at 95% CL from Fig1(f) for $\tilde{g}$ decays into anti-top and $\tilde{t}$ and $\tilde{t}$ decays via a non-zero RPV coupling $\lambda''$
Cross-section upper limits at 95% CL from Fig1(e) for direct $\tilde{\chi_{1}^{0}}$ decay into SM leptons and quarks via a non-zero RPV coupling $\lambda'$
Cross-section upper limits at 95% CL from Fig1(b) for $\tilde{q}$ decays into SM gauge bosons and $\tilde{\chi}^{0}_{1}$
Cross-section upper limits at 95% CL from Fig1(d) for $\tilde{q}$ decays into sleptons and subsequently to SM leptons and $\tilde{\chi}^{0}_{1}$
A search for supersymmetry targeting the direct production of winos and higgsinos is conducted in final states with either two leptons ($e$ or $\mu$) with the same electric charge, or three leptons. The analysis uses 139 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}=13$ TeV collected with the ATLAS detector during Run 2 of the Large Hadron Collider. No significant excess over the Standard Model expectation is observed. Simplified and complete models with and without $R$-parity conservation are considered. In topologies with intermediate states including either $Wh$ or $WZ$ pairs, wino masses up to 525 GeV and 250 GeV are excluded, respectively, for a bino of vanishing mass. Higgsino masses smaller than 440 GeV are excluded in a natural $R$-parity-violating model with bilinear terms. Upper limits on the production cross section of generic events beyond the Standard Model as low as 40 ab are obtained in signal regions optimised for these models and also for an $R$-parity-violating scenario with baryon-number-violating higgsino decays into top quarks and jets. The analysis significantly improves sensitivity to supersymmetric models and other processes beyond the Standard Model that may contribute to the considered final states.
Observed exclusion limits at 95% CL for the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 13(b) and Fig 8(aux).
positive one $\sigma$ observed exclusion limits at 95% CL for the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 13(b) and Fig 8(aux).
negative $\sigma$ variation of observed exclusion limits at 95% CL for the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 13(b) and Fig 8(aux).
Observed excluded cross-section at 95% CL for the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 8(aux).
Expected exclusion limits at 95% CL for the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production.
Observed exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from from Fig 13(a) and from Fig 7 and Fig 10(aux).
Observed excluded cross-section at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 7(aux) and Fig 10(aux).
positive one $\sigma$ observed exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from from Fig 13(a) and from Fig 7 and Fig 10(aux).
negative one $\sigma$ observed exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from from Fig 13(a) and from Fig 7 and Fig 10(aux).
Expected exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production.
Expected exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production.
Expected exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region $SR^{bRPV}_{2l-SS}$. in a susy scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV, tan$\beta$=5. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region $SR^{bRPV}_{3l}$. in a susy scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV, tan$\beta$=5. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region $SR^{WZ}_{high-m_{T2}}$. The wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{\pm} _{1}/\tilde{\chi}^{0} _{2})$ = 150 GeV, $m(\tilde{\chi}^{0} _{1})$ = 50 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region $SR^{WZ}_{low-m_{T2}}$. The wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{\pm} _{1}/\tilde{\chi}^{0} _{2})$ = 150 GeV, $m(\tilde{\chi}^{0} _{1})$ = 50 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the low mass $SR^{RPV}_{2l1b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the medium mass $SR^{RPV}_{2l1b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the low mass $SR^{RPV}_{2l2b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the medium mass $SR^{RPV}_{2l2b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the high mass $SR^{RPV}_{2l2b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the low mass $SR^{RPV}_{2l3b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the medium mass $SR^{RPV}_{2l3b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the high mass $SR^{RPV}_{2l3b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the $SR^{Wh}_{low-m_{T2} }$. The wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Higgs bosons. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{\pm} _{1}/\tilde{\chi}^{0} _{2})$ = 300 GeV, $m(\tilde{\chi}^{0} _{1})$ = 100 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the $SR^{Wh}_{high-m_{T2} }$. The wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Higgs bosons. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{\pm} _{1}/\tilde{\chi}^{0} _{2})$ = 300 GeV, $m(\tilde{\chi}^{0} _{1})$ = 100 GeV. Only statistical uncertainties are shown.
Signal Hepdataeptance for $SR^{bRPV}_{2l-SS}$ signal region from Fig 13(a)(aux) in a SUSY scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays.
Signal Hepdataeptance for $SR^{bRPV}_{3l}$ signal region from Fig 13(b)(aux) in a SUSY scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays.
Signal acceptance for $SR^{WZ}_{high-m_{T2}}$ in a SUSY scenario where the wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons.
Signal acceptance for $SR^{WZ}_{low-m_{T2}}$ in a SUSY scenario where the wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons.
Signal acceptance for $SR^{RPV}_{2l1b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal acceptance for $SR^{RPV}_{2l1b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal acceptance for $SR^{RPV}_{2l2b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal acceptance for $SR^{RPV}_{2l2b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal acceptance for $SR^{RPV}_{2l2b}-H$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal acceptance for $SR^{RPV}_{2l3b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal acceptance for $SR^{RPV}_{2l3b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal acceptance for $SR^{RPV}_{2l3b}-H$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal efficiency for $SR^{bRPV}_{2l-SS}$ signal region in a SUSY scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays.
Signal efficiency for $SR^{bRPV}_{3l}$ signal region in a SUSY scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays.
Signal efficiency for $SR^{WZ}_{high-m_{T2}}$ in a SUSY scenario where the wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons.
Signal efficiency for $SR^{WZ}_{low-m_{T2}}$ in a SUSY scenario where the wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons.
Signal efficiency for $SR^{RPV}_{2l1b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal efficiency for $SR^{RPV}_{2l1b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal efficiency for $SR^{RPV}_{2l2b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal efficiency for $SR^{RPV}_{2l2b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal efficiency for $SR^{RPV}_{2l2b}-H$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal efficiency for $SR^{RPV}_{2l3b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal efficiency for $SR^{RPV}_{2l3b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal efficiency for $SR^{RPV}_{2l3b}-H$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.
Signal acceptance for $SR^{Wh}_{high-m_{T2} }$ signal region from Fig 11(a)(aux) in a SUSY scenario where direct production of a lightest $\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$ , decay with 100% branching ratio to a final state with a same sign light lepton (e or $\mu$) pair and two lightest neutralino1, via the on-shell emission of SM W and Higgs bosons,
Signal acceptance for $SR^{Wh}_{low-m_{T2} }$ signal region from Fig 11(b)(aux) in a SUSY scenario where direct production of a lightest $\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$ , decay with 100% branching ratio to a final state with a same sign light lepton (e or $\mu$) pair and two lightest neutralino1, via the on-shell emission of SM W and Higgs bosons,
Signal efficiency for $SR^{Wh}_{high-m_{T2} }$ signal region from Fig 15(a)(aux) in a SUSY scenario where direct production of a lightest $\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$ , decay with 100% branching ratio to a final state with a same sign light lepton (e or $\mu$) pair and two lightest neutralino1, via the on-shell emission of SM W and Higgs bosons,
Signal efficiency for $SR^{Wh}_{low-m_{T2} }$ signal region from Fig 15(b)(aux) in a SUSY scenario where direct production of a lightest $\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$ , decay with 100% branching ratio to a final state with a same sign light lepton (e or $\mu$) pair and two lightest neutralino1, via the on-shell emission of SM W and Higgs bosons,
Observed 95% X-section upper limits as a function of higgsino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{1}/\tilde{\chi}^{0}_{2}$ mass in the bilinear RPV model from Fig 14.
Observed 95% X-section upper limits as a function of higgsino $\tilde{\chi}^{0}_{1}/\tilde{\chi}^{0}_{2}$ mass in the UDD RPV model from Fig 18.
Observed 95% X-section upper limits as a function of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ mass in the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 9(aux).
N-1 distributions for $m_{T2}$ of observed data and expected background towards $SR^{WZ}_{high-m_{T2}}$ from publication's Figure 11(a) . The last bin is inclusive.
N-1 distributions for $m_{T2}$ of observed data and expected background towards $SR^{WZ}_{low-m_{T2}}$ from publication's Figure 11(b) . The last bin is inclusive.
N-1 distributions for $m_{T2}$ of observed data and expected background towards $SR^{bRPV}_{2l-SS}$ from publication's Figure 11(c) . The last bin is inclusive.
N-1 distributions for $m_{T2}$ of observed data and expected background towards $SR^{bRPV}_{3l}$ from publication's Figure 11(d) . The last bin is inclusive.
N-1 distributions for $\sum p^{b-jet}_{T}/\sum p^{jet}_{T}$ of observed data and expected background towards $SR^{RPV}_{2l1b}-L$ from publication's Figure 16(a) . The last bin is inclusive.
N-1 distributions for $\sum p^{b-jet}_{T}/\sum p^{jet}_{T}$ of observed data and expected background towards $SR^{RPV}_{2l2b}-M$ from publication's Figure 16(b) . The last bin is inclusive.
N-1 distributions for $\sum p^{b-jet}_{T}/\sum p^{jet}_{T}$ of observed data and expected background towards $SR^{RPV}_{2l3b}-H$ from publication's Figure 16(c) . The last bin is inclusive.
N-1 distribution for $E_{T}^{miss}$ in $SR^{Wh}_{high-m_{T2} }$ in ee channel
N-1 distribution for $E_{T}^{miss}$ in $SR^{Wh}_{high-m_{T2} }$ in e$\mu$ channel
N-1 distribution for $E_{T}^{miss}$ in $SR^{Wh}_{high-m_{T2} }$ in $\mu\mu$ channel
N-1 distribution for $\mathcal{S}(E_{T}^{miss})$ in $SR^{Wh}_{low-m_{T2} }$ in ee channel
N-1 distribution for $\mathcal{S}(E_{T}^{miss})$ in $SR^{Wh}_{low-m_{T2} }$ in e$\mu$ channel
N-1 distribution for $\mathcal{S}(E_{T}^{miss})$ in $SR^{Wh}_{low-m_{T2} }$ in $\mu\mu$ channel
A search is presented for displaced production of Higgs bosons or $Z$ bosons, originating from the decay of a neutral long-lived particle (LLP) and reconstructed in the decay modes $H\rightarrow \gamma\gamma$ and $Z\rightarrow ee$. The analysis uses the full Run 2 data set of proton$-$proton collisions delivered by the LHC at an energy of $\sqrt{s}=13$ TeV between 2015 and 2018 and recorded by the ATLAS detector, corresponding to an integrated luminosity of 139 fb$^{-1}$. Exploiting the capabilities of the ATLAS liquid argon calorimeter to precisely measure the arrival times and trajectories of electromagnetic objects, the analysis searches for the signature of pairs of photons or electrons which arise from a common displaced vertex and which arrive after some delay at the calorimeter. The results are interpreted in a gauge-mediated supersymmetry breaking model with pair-produced higgsinos that decay to LLPs, and each LLP subsequently decays into either a Higgs boson or a $Z$ boson. The final state includes at least two particles that escape direct detection, giving rise to missing transverse momentum. No significant excess is observed above the background expectation. The results are used to set upper limits on the cross section for higgsino pair production, up to a $\tilde\chi^0_1$ mass of 369 (704) GeV for decays with 100% branching ratio of $\tilde\chi^0_1$ to Higgs ($Z$) bosons for a $\tilde\chi^0_1$ lifetime of 2 ns. A model-independent limit is also set on the production of pairs of photons or electrons with a significant delay in arrival at the calorimeter.
Average timing distributions for SR data and the estimated background as determined by the background-only fit, in each of the five exclusive $\rho$ categories. For comparison, the expected timing shapes for a few different signal models are superimposed, with each model labeled by the values of the $\tilde\chi^0_1$ mass and lifetime, as well as decay mode. To provide some indication of the variations in signal yield and shape, three signal models are shown for each of the $\tilde\chi^0_1$ decay modes, namely $\tilde\chi^0_1$ $\rightarrow$ $H \tilde G$ and $\tilde\chi^0_1$ $\rightarrow$ $Z \tilde G$. The models shown include a rather low $\tilde\chi^0_1$ mass value of 135 GeV for lifetimes of either 2 ns or 10 ns, and a higher $\tilde\chi^0_1$ mass value which is near the 95% CL exclusion limit for each decay mode for a lifetime of 2 ns. Each signal model is shown with the signal normalization corresponding to a BR value of unity for the decay mode in question.
Average timing distributions for SR data and the estimated background as determined by the background-only fit, in each of the five exclusive $\rho$ categories. For comparison, the expected timing shapes for a few different signal models are superimposed, with each model labeled by the values of the $\tilde\chi^0_1$ mass and lifetime, as well as decay mode. To provide some indication of the variations in signal yield and shape, three signal models are shown for each of the $\tilde\chi^0_1$ decay modes, namely $\tilde\chi^0_1$ $\rightarrow$ $H \tilde G$ and $\tilde\chi^0_1$ $\rightarrow$ $Z \tilde G$. The models shown include a rather low $\tilde\chi^0_1$ mass value of 135 GeV for lifetimes of either 2 ns or 10 ns, and a higher $\tilde\chi^0_1$ mass value which is near the 95% CL exclusion limit for each decay mode for a lifetime of 2 ns. Each signal model is shown with the signal normalization corresponding to a BR value of unity for the decay mode in question.
Average timing distributions for SR data and the estimated background as determined by the background-only fit, in each of the five exclusive $\rho$ categories. For comparison, the expected timing shapes for a few different signal models are superimposed, with each model labeled by the values of the $\tilde\chi^0_1$ mass and lifetime, as well as decay mode. To provide some indication of the variations in signal yield and shape, three signal models are shown for each of the $\tilde\chi^0_1$ decay modes, namely $\tilde\chi^0_1$ $\rightarrow$ $H \tilde G$ and $\tilde\chi^0_1$ $\rightarrow$ $Z \tilde G$. The models shown include a rather low $\tilde\chi^0_1$ mass value of 135 GeV for lifetimes of either 2 ns or 10 ns, and a higher $\tilde\chi^0_1$ mass value which is near the 95% CL exclusion limit for each decay mode for a lifetime of 2 ns. Each signal model is shown with the signal normalization corresponding to a BR value of unity for the decay mode in question.
Average timing distributions for SR data and the estimated background as determined by the background-only fit, in each of the five exclusive $\rho$ categories. For comparison, the expected timing shapes for a few different signal models are superimposed, with each model labeled by the values of the $\tilde\chi^0_1$ mass and lifetime, as well as decay mode. To provide some indication of the variations in signal yield and shape, three signal models are shown for each of the $\tilde\chi^0_1$ decay modes, namely $\tilde\chi^0_1$ $\rightarrow$ $H \tilde G$ and $\tilde\chi^0_1$ $\rightarrow$ $Z \tilde G$. The models shown include a rather low $\tilde\chi^0_1$ mass value of 135 GeV for lifetimes of either 2 ns or 10 ns, and a higher $\tilde\chi^0_1$ mass value which is near the 95% CL exclusion limit for each decay mode for a lifetime of 2 ns. Each signal model is shown with the signal normalization corresponding to a BR value of unity for the decay mode in question.
Average timing distributions for SR data and the estimated background as determined by the background-only fit, in each of the five exclusive $\rho$ categories. For comparison, the expected timing shapes for a few different signal models are superimposed, with each model labeled by the values of the $\tilde\chi^0_1$ mass and lifetime, as well as decay mode. To provide some indication of the variations in signal yield and shape, three signal models are shown for each of the $\tilde\chi^0_1$ decay modes, namely $\tilde\chi^0_1$ $\rightarrow$ $H \tilde G$ and $\tilde\chi^0_1$ $\rightarrow$ $Z \tilde G$. The models shown include a rather low $\tilde\chi^0_1$ mass value of 135 GeV for lifetimes of either 2 ns or 10 ns, and a higher $\tilde\chi^0_1$ mass value which is near the 95% CL exclusion limit for each decay mode for a lifetime of 2 ns. Each signal model is shown with the signal normalization corresponding to a BR value of unity for the decay mode in question.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ mass (left) and $\tilde\chi^0_1$ lifetime (right), for the different decay modes of $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$) = 1 (top) and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 (bottom). For the limits as a function of mass (lifetime), several signal models with varying lifetime (mass) are overlaid for comparison. Included are the theoretical expectations from higgsino production for each mass hypothesis, calculated from a GMSB SUSY model that assumes nearly degenerate $\tilde\chi^0_1$, $\tilde\chi^\pm_1$, and $\tilde\chi^0_2$.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ branching ratio to the SM Higgs boson, where the assumed cross-section is for higgsino production, and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 - $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$). Several signal hypotheses are overlaid that are labelled by the $\tilde\chi^0_1$ mass, all with a fixed $\tilde\chi^0_1$ lifetime of 2 ns.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ branching ratio to the SM Higgs boson, where the assumed cross-section is for higgsino production, and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 - $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$). Several signal hypotheses are overlaid that are labelled by the $\tilde\chi^0_1$ mass, all with a fixed $\tilde\chi^0_1$ lifetime of 2 ns.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ branching ratio to the SM Higgs boson, where the assumed cross-section is for higgsino production, and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 - $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$). Several signal hypotheses are overlaid that are labelled by the $\tilde\chi^0_1$ mass, all with a fixed $\tilde\chi^0_1$ lifetime of 2 ns.
The 95% CL limits on $\sigma(pp \rightarrow \tilde\chi^0_1 \tilde\chi^0_1$) in fb as a function of $\tilde\chi^0_1$ branching ratio to the SM Higgs boson, where the assumed cross-section is for higgsino production, and $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow Z +\tilde{G}$) = 1 - $\mathcal{B}$($\tilde\chi^0_1$ $\rightarrow H + \tilde{G}$). Several signal hypotheses are overlaid that are labelled by the $\tilde\chi^0_1$ mass, all with a fixed $\tilde\chi^0_1$ lifetime of 2 ns.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
The 95% CL exclusion limits on the target signal hypothesis, for $\tilde\chi^0_1$ lifetime in ns as a function of $\tilde\chi^0_1$ mass in GeV. The overlaid curves correspond to different decay hypotheses, where the assumed cross-section is for higgsino production, and the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ or $Z + \tilde{G}$ such that $\mathcal{B}(H + \tilde{G}) + \mathcal{B}(Z + \tilde{G})$ = 100%. The curve shown in red represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $Z + \tilde{G}$ with 100% branching ratio. The curve shown in blue represents the decay hypothesis where the $\tilde\chi^0_1$ decays to $H + \tilde{G}$ with 100% branching ratio.
Cutflow for an example higgsino signal with mass 225 GeV and lifetime 10 ns, in the H decay mode. Acceptance is defined at truth level, and efficiency compares the events passing at reconstruction level with respect to truth.
Cutflow for an example higgsino signal with mass 225 GeV and lifetime 10 ns, in the Z decay mode. Acceptance is defined at truth level, and efficiency compares the events passing at reconstruction level with respect to truth.
Cutflow for an example higgsino signal with mass 225 GeV and lifetime 2 ns, in the H decay mode. Acceptance is defined at truth level, and efficiency compares the events passing at reconstruction level with respect to truth.
Cutflow for an example higgsino signal with mass 225 GeV and lifetime 2 ns, in the Z decay mode. Acceptance is defined at truth level, and efficiency compares the events passing at reconstruction level with respect to truth.
Acceptance across the H decay mode signal grid, calculated using truth information. The selection applied corresponds to the model-independent signal region (i.e. the standard SR with $t_{\text{avg}$ > 0.9 ns).
Acceptance across the Z decay mode signal grid, calculated using truth information. The selection applied corresponds to the model-independent signal region (i.e. the standard SR with $t_{\text{avg}$ > 0.9 ns).
Efficiency across the H decay mode signal grid, calculated using reco information. The selection applied corresponds to the model-independent signal region (i.e. the standard SR with $t_{\text{avg}$ > 0.9 ns). Here, the numerator is the signal yield passing the reco selection and the denominator is the signal yield passing the truth selection.
Efficiency across the Z decay mode signal grid, calculated using reco information. The selection applied corresponds to the model-independent signal region (i.e. the standard SR with $t_{\text{avg}$ > 0.9 ns). Here, the numerator is the signal yield passing the reco selection and the denominator is the signal yield passing the truth selection.
A search for long-lived particles decaying into hadrons is presented. The analysis uses 139 fb$^{-1}$ of $pp$ collision data collected at $\sqrt{s} = 13$ TeV by the ATLAS detector at the LHC using events that contain multiple energetic jets and a displaced vertex. The search employs dedicated reconstruction techniques that significantly increase the sensitivity to long-lived particles decaying in the ATLAS inner detector. Background estimates for Standard Model processes and instrumental effects are extracted from data. The observed event yields are compatible with those expected from background processes. The results are used to set limits at 95% confidence level on model-independent cross sections for processes beyond the Standard Model, and on scenarios with pair-production of supersymmetric particles with long-lived electroweakinos that decay via a small $R$-parity-violating coupling. The pair-production of electroweakinos with masses below 1.5 TeV is excluded for mean proper lifetimes in the range from 0.03 ns to 1 ns. When produced in the decay of $m(\tilde{g})=2.4$ TeV gluinos, electroweakinos with $m(\tilde\chi^0_1)=1.5$ TeV are excluded with lifetimes in the range of 0.02 ns to 4 ns.
A search for long-lived particles decaying in the outer regions of the CMS silicon tracker or in the calorimeters is presented. The search is based on a data sample of proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded with the CMS detector at the LHC in 2016-2018, corresponding to an integrated luminosity of 138 fb$^{-1}$. A novel technique, using trackless and out-of-time jet information combined in a deep neural network discriminator, is employed to identify decays of long-lived particles. The results are interpreted in a simplified model of chargino-neutralino production, where the neutralino is the next-to-lightest supersymmetric particle, is long-lived, and decays to a gravitino and either a Higgs or Z boson. This search is most sensitive to neutralino proper decay lengths of approximately 0.5 m, for which masses up to 1.18 TeV are excluded at 95% confidence level. The current search is the best result to date in the mass range from the kinematic limit imposed by the Higgs mass up to 1.8 TeV.
Summary of combined statistical and systematic uncertainties, the size of their effect, and whether it applies to the signal or background yield predictions. Ranges for signal systematic uncertainties reflect their impact on different signal parameter space points.
Summary of combined statistical and systematic uncertainties, the size of their effect, and whether it applies to the signal or background yield predictions. Ranges for signal systematic uncertainties reflect their impact on different signal parameter space points.
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker.
TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events.
TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker.
TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events.
The TD jet tagger score distributions for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The last bin contains jets with tagger scores greater than 0.996, the threshold used to tag signal jets. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The efficiency of the TD jet tagger working point used in the analysis is shown as a function of the lab frame transverse decay length for simulated signals with $\chi$ mass of 400 GeV. The uncertainties shown account for lifetime dependence and statistical uncertainty.
The TD jet tagger misidentification probability measured using the nominal $W$+jets MR is shown along with the systematic uncertainty, quantifying the degree of process dependence measured from alternative MRs. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.
The TD jet tagger score distributions for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. The last bin contains jets with tagger scores greater than 0.996, the threshold used to tag signal jets. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The TD jet tagger misidentification probability measured using the nominal $W$+jets MR is shown along with the systematic uncertainty, quantifying the degree of process dependence measured from alternative MRs. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.
The TD jet tagger misidentification probability measured using the nominal $W$+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well ($Z$+jets MR as green round markers, $t\bar{t}$ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.
Distribution of the number of TD tagged jets for the $m_{\chi} = 400$ GeVsimulated signal samples with $c\tau_{\chi} = 0.5$ m (solid red line) and $c\tau_{\chi} = 3.0$ m (dotted green line), estimated background (blue square markers), and data (black round markers). The blue shaded region indicates the systematic uncertainty in the background prediction. No background prediction is shown for the bin with zero TD tagged jets as it is the main control region used to predict the background for the other two bins. There are zero observed events in the bin with two or more TD tagged jets.
The TD jet tagger misidentification probability measured using the nominal $W$+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well ($Z$+jets MR as green round markers, $t\bar{t}$ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).
Distribution of the number of TD tagged jets for the $m_{\chi} = 400$ GeVsimulated signal samples with $c\tau_{\chi} = 0.5$ m (solid red line) and $c\tau_{\chi} = 3.0$ m (dotted green line), estimated background (blue square markers), and data (black round markers). The signal distributions are normalized to the expected cross section limit. The blue shaded region indicates the systematic uncertainty in the background prediction. No background prediction is shown for the bin with zero TD tagged jets as it is the main control region used to predict the background for the other two bins. There are zero observed events in the bin with two or more TD tagged jets.
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$. The area enclosed by the dotted black line corresponds to the observed excluded region.
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The distribution of the jet charged hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The distribution of the jet neutral hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The distribution of the number of track constituents in the jet, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The $\eta$ distribution of TD-tagged jets in a background-enriched control region that comprises events with exactly one TD-tagged jet. Observed data (black round markers) and the corresponding prediction based on control samples in data (empty squared markers), measured using the nominal $W$+jets MR, are compared. The prediction uncertainty (gray band) includes the systematic uncertainty quantifying the degree of process dependence measured from alternative MRs. The predictions for the shape and the normalization of the $\eta$ distribution are consistent with the data.
Jet time distribution in a sample of b-tagged jets from dilepton $t \bar{t}$ events in 2017 data-taking period (black round markers) and simulation (filled histogram). A Gaussian smearing procedure is applied to the jet time in the $t \bar{t}$ sample (green line) to correct for effects that are difficult to simulate (timing drift caused by crystals transparency loss due to detector aging, electronics jitter).
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 1$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.75$, $\mathcal{B}(\chi\to Z\tilde{G}) = 0.25$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.25$, $\mathcal{B}(\chi\to Z\tilde{G}) = 0.75$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to Z\tilde{G}) = 1$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 127 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 127 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 150 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 150 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 175 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 175 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 200 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 200 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 250 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 250 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 300 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 300 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 400 GeV.
A search for supersymmetry involving the pair production of gluinos decaying via off-shell third-generation squarks into the lightest neutralino ($\tilde\chi^0_1$) is reported. It exploits LHC proton$-$proton collision data at a centre-of-mass energy $\sqrt{s} = 13$ TeV with an integrated luminosity of 139 fb$^{-1}$ collected with the ATLAS detector from 2015 to 2018. The search uses events containing large missing transverse momentum, up to one electron or muon, and several energetic jets, at least three of which must be identified as containing $b$-hadrons. Both a simple kinematic event selection and an event selection based upon a deep neural-network are used. No significant excess above the predicted background is found. In simplified models involving the pair production of gluinos that decay via off-shell top (bottom) squarks, gluino masses less than 2.44 TeV (2.35 TeV) are excluded at 95% CL for a massless $\tilde\chi^0_1$. Limits are also set on the gluino mass in models with variable branching ratios for gluino decays to $b\bar{b}\tilde\chi^0_1$, $t\bar{t}\tilde\chi^0_1$ and $t\bar{b}\tilde\chi^-_1$ / $\bar{t}b\tilde\chi^+_1$.
A summary of the uncertainties in the background estimates for SR-Gtt-0L-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-0L-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-0L-M1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-0L-M1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-0L-M2. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-0L-M2. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-0L-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-0L-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1L-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1L-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1L-M1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1L-M1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1L-M2. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1L-M2. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1L-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1L-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-M. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-M. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtb-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtb-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtb-M. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtb-M. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtb-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtb-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-2100-1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-2100-1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1800-1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1800-1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-2300-1200. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-2300-1200. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1900-1400. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gtt-1900-1400. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-2800-1400. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-2800-1400. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-2300-1000. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-2300-1000. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-2100-1600. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-2100-1600. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-2000-1800. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
A summary of the uncertainties in the background estimates for SR-Gbb-2000-1800. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.
Results of the background-only fit extrapolated to SR_Gtt_0L_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_0L_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_0L_M1 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_0L_M1 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_0L_M2 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_0L_M2 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_0L_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_0L_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1L_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1L_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1L_M1 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1L_M1 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1L_M2 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1L_M2 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1L_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1L_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_M in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_M in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtb_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtb_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtb_M in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtb_M in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtb_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtb_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_2100_1 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_2100_1 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1800_1 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1800_1 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_2300_1200 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_2300_1200 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1900_1400 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gtt_1900_1400 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_2800_1400 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_2800_1400 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_2300_1000 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_2300_1000 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_2100_1600 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_2100_1600 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_2000_1800 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Results of the background-only fit extrapolated to SR_Gbb_2000_1800 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~GeV, obtained from the CC analysis.
Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~GeV, obtained from the CC analysis.
Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~GeV, obtained from the CC analysis.
Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~GeV, obtained from the CC analysis.
Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 600$~GeV, obtained from the CC analysis.
Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 600$~GeV, obtained from the CC analysis.
Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 600$~GeV, obtained from the CC analysis.
Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 600$~GeV, obtained from the CC analysis.
Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~TeV, obtained from the CC analysis.
Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~TeV, obtained from the CC analysis.
Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~TeV, obtained from the CC analysis.
Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~TeV, obtained from the CC analysis.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.
Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb (right) models obtained from the CC analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.
Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb (right) models obtained from the CC analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.
Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb (right) models obtained from the NN analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.
Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb (right) models obtained from the NN analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.
Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt (left) models obtained from the CC analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.
Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt (left) models obtained from the CC analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.
Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt (left) models obtained from the NN analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.
Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt (left) models obtained from the NN analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.
Acceptance for SR-Gtt-0L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-0L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-0L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-0L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-0L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-0L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-0L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-0L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-0L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-0L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-0L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-0L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-0L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-0L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-0L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-0L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-B and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-B and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-B and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-B and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-M and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-M and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-M and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-M and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-C and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-C and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-C and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-C and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-2100-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-2100-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-2100-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-2100-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1800-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1800-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-2300-1200 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-2300-1200 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gtt-1900-1400 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gtt-1900-1400 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-2800-1400 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-2800-1400 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-2300-1000 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-2300-1000 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-2100-1600 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-2100-1600 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Acceptance for SR-Gbb-2000-1800 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Efficiency for SR-Gbb-2000-1800 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.
Cutflow for the SR-Gtt-0L-B for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-0L-M1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-0L-M2 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-0L-C for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-1L-B for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-1L-M1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-1L-M2 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-1L-C for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gbb-B for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gbb-M for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gbb-C for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtb-B for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtb-M for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtb-C for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-2100-1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-1800-1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-2300-1200 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gtt-1900-1400 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gbb-2800-1400 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gbb-2300-1000 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gbb-2100-1600 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
Cutflow for the SR-Gbb-2000-1800 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.
This paper presents a statistical combination of searches targeting final states with two top quarks and invisible particles, characterised by the presence of zero, one or two leptons, at least one jet originating from a $b$-quark and missing transverse momentum. The analyses are searches for phenomena beyond the Standard Model consistent with the direct production of dark matter in $pp$ collisions at the LHC, using 139 fb$^{-\text{1}}$ of data collected with the ATLAS detector at a centre-of-mass energy of 13 TeV. The results are interpreted in terms of simplified dark matter models with a spin-0 scalar or pseudoscalar mediator particle. In addition, the results are interpreted in terms of upper limits on the Higgs boson invisible branching ratio, where the Higgs boson is produced according to the Standard Model in association with a pair of top quarks. For scalar (pseudoscalar) dark matter models, with all couplings set to unity, the statistical combination extends the mass range excluded by the best of the individual channels by 50 (25) GeV, excluding mediator masses up to 370 GeV. In addition, the statistical combination improves the expected coupling exclusion reach by 14% (24%), assuming a scalar (pseudoscalar) mediator mass of 10 GeV. An upper limit on the Higgs boson invisible branching ratio of 0.38 (0.30$^{+\text{0.13}}_{-\text{0.09}}$) is observed (expected) at 95% confidence level.
A search for the electroweak production of pairs of charged sleptons or charginos decaying into two-lepton final states with missing transverse momentum is presented. Two simplified models of $R$-parity-conserving supersymmetry are considered: direct pair-production of sleptons ($\tilde{\ell}\tilde{\ell}$), with each decaying into a charged lepton and a $\tilde{\chi}_1^0$ neutralino, and direct pair-production of the lightest charginos $(\tilde{\chi}_1^\pm\tilde{\chi}_1^\mp)$, with each decaying into a $W$-boson and a $\tilde{\chi}_1^0$. The lightest neutralino ($\tilde{\chi}_1^0$) is assumed to be the lightest supersymmetric particle (LSP). The analyses target the experimentally challenging mass regions where $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and $m(\tilde{\chi}_1^\pm)-m(\tilde{\chi}_1^0)$ are close to the $W$-boson mass (`moderately compressed' regions). The search uses 139 fb$^{-1}$ of $\sqrt{s}=13$ TeV proton-proton collisions recorded by the ATLAS detector at the Large Hadron Collider. No significant excesses over the expected background are observed. Exclusion limits on the simplified models under study are reported in the ($\tilde{\ell},\tilde{\chi}_1^0$) and ($\tilde{\chi}_1^\pm,\tilde{\chi}_1^0$) mass planes at 95% confidence level (CL). Sleptons with masses up to 150 GeV are excluded at 95% CL for the case of a mass-splitting between sleptons and the LSP of 50 GeV. Chargino masses up to 140 GeV are excluded at 95% CL for the case of a mass-splitting between the chargino and the LSP down to about 100 GeV.
<b>- - - - - - - - Overview of HEPData Record - - - - - - - -</b> <b>Title: </b><em>Search for direct pair production of sleptons and charginos decaying to two leptons and neutralinos with mass splittings near the $W$ boson mass in $\sqrt{s}=13$ TeV $pp$ collisions with the ATLAS detector</em> <b>Paper website:</b> <a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SUSY-2019-02/">SUSY-2019-02</a> <b>Exclusion contours</b> <ul><li><b>Sleptons:</b> <a href=?table=excl_comb_obs_nominal>Combined Observed Nominal</a> <a href=?table=excl_comb_obs_up>Combined Observed Up</a> <a href=?table=excl_comb_obs_down>Combined Observed Down</a> <a href=?table=excl_comb_exp_nominal>Combined Expected Nominal</a> <a href=?table=excl_comb_exp_up>Combined Expected Up</a> <a href=?table=excl_comb_exp_down>Combined Expected Down</a> <a href=?table=excl_comb_obs_nominal_dM>Combined Observed Nominal $(\Delta m)$</a> <a href=?table=excl_comb_obs_up_dM>Combined Observed Up $(\Delta m)$</a> <a href=?table=excl_comb_obs_down_dM>Combined Observed Down $(\Delta m)$</a> <a href=?table=excl_comb_exp_nominal_dM>Combined Expected Nominal $(\Delta m)$</a> <a href=?table=excl_comb_exp_up_dM>Combined Expected Up $(\Delta m)$</a> <a href=?table=excl_comb_exp_down_dM>Combined Expected Down $(\Delta m)$</a> <a href=?table=excl_ee_obs_nominal>$\tilde{e}_\mathrm{L,R}$ Observed Nominal</a> <a href=?table=excl_ee_exp_nominal>$\tilde{e}_\mathrm{L,R}$ Expected Nominal</a> <a href=?table=excl_eLeL_obs_nominal>$\tilde{e}_\mathrm{L}$ Observed Nominal</a> <a href=?table=excl_eLeL_exp_nominal>$\tilde{e}_\mathrm{L}$ Expected Nominal</a> <a href=?table=excl_eReR_obs_nominal>$\tilde{e}_\mathrm{R}$ Observed Nominal</a> <a href=?table=excl_eReR_exp_nominal>$\tilde{e}_\mathrm{R}$ Expected Nominal</a> <a href=?table=excl_ee_obs_nominal_dM>$\tilde{e}_\mathrm{L,R}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_ee_exp_nominal_dM>$\tilde{e}_\mathrm{L,R}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_eLeL_obs_nominal_dM>$\tilde{e}_\mathrm{L}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_eLeL_exp_nominal_dM>$\tilde{e}_\mathrm{L}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_eReR_obs_nominal_dM>$\tilde{e}_\mathrm{R}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_eReR_exp_nominal_dM>$\tilde{e}_\mathrm{R}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_mm_obs_nominal>$\tilde{\mu}_\mathrm{L,R}$ Observed Nominal</a> <a href=?table=excl_mm_exp_nominal>$\tilde{\mu}_\mathrm{L,R}$ Expected Nominal</a> <a href=?table=excl_mLmL_obs_nominal>$\tilde{\mu}_\mathrm{L}$ Observed Nominal</a> <a href=?table=excl_mLmL_exp_nominal>$\tilde{\mu}_\mathrm{L}$ Expected Nominal</a> <a href=?table=excl_mRmR_obs_nominal>$\tilde{\mu}_\mathrm{R}$ Observed Nominal</a> <a href=?table=excl_mRmR_exp_nominal>$\tilde{\mu}_\mathrm{R}$ Expected Nominal</a> <a href=?table=excl_mm_obs_nominal_dM>$\tilde{\mu}_\mathrm{L,R}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_mm_exp_nominal_dM>$\tilde{\mu}_\mathrm{L,R}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_mLmL_obs_nominal_dM>$\tilde{\mu}_\mathrm{L}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_mLmL_exp_nominal_dM>$\tilde{\mu}_\mathrm{L}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_mRmR_obs_nominal_dM>$\tilde{\mu}_\mathrm{R}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_mRmR_exp_nominal_dM>$\tilde{\mu}_\mathrm{R}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_comb_obs_nominal_SR0j>Combined Observed Nominal SR-0j</a> <a href=?table=excl_comb_exp_nominal_SR0j>Combined Expected Nominal SR-0j</a> <a href=?table=excl_comb_obs_nominal_SR1j>Combined Observed Nominal SR-1j</a> <a href=?table=excl_comb_exp_nominal_SR1j>Combined Expected Nominal SR-1j</a> <li><b>Charginos:</b> <a href=?table=excl_c1c1_obs_nominal>Observed Nominal</a> <a href=?table=excl_c1c1_obs_up>Observed Up</a> <a href=?table=excl_c1c1_obs_down>Observed Down</a> <a href=?table=excl_c1c1_exp_nominal>Expected Nominal</a> <a href=?table=excl_c1c1_exp_nominal>Expected Up</a> <a href=?table=excl_c1c1_exp_nominal>Expected Down</a> <a href=?table=excl_c1c1_obs_nominal_dM>Observed Nominal $(\Delta m)$</a> <a href=?table=excl_c1c1_obs_up_dM>Observed Up $(\Delta m)$</a> <a href=?table=excl_c1c1_obs_down_dM>Observed Down $(\Delta m)$</a> <a href=?table=excl_c1c1_exp_nominal_dM>Expected Nominal $(\Delta m)$</a> <a href=?table=excl_c1c1_exp_nominal_dM>Expected Up $(\Delta m)$</a> <a href=?table=excl_c1c1_exp_nominal_dM>Expected Down $(\Delta m)$</a> </ul> <b>Upper Limits</b> <ul><li><b>Sleptons:</b> <a href=?table=UL_slep>ULs</a> <li><b>Charginos:</b> <a href=?table=UL_c1c1>ULs</a> </ul> <b>Pull Plots</b> <ul><li><b>Sleptons:</b> <a href=?table=pullplot_slep>SRs summary plot</a> <li><b>Charginos:</b> <a href=?table=pullplot_c1c1>SRs summary plot</a> </ul> <b>Cutflows</b> <ul><li><b>Sleptons:</b> <a href=?table=Cutflow_slep_SR0j>Towards SR-0J</a> <a href=?table=Cutflow_slep_SR1j>Towards SR-1J</a> <li><b>Charginos:</b> <a href=?table=Cutflow_SRs>Towards SRs</a> </ul> <b>Acceptance and Efficiencies</b> <ul><li><b>Sleptons:</b> <a href=?table=Acceptance_SR0j_MT2_100_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_100_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_110_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_110_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_120_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_120_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_130_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_130_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_100_105>SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_100_105>SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_105_110>SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_105_110>SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_110_115>SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_110_115>SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_115_120>SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_115_120>SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_120_125>SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_125_130>SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_130_140>SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_130_140>SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_140_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_140_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_100_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[100,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_100_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[100,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_110_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[110,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_110_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[110,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_120_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[120,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_120_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[120,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_130_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[130,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_130_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[130,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_100_105>SR-1j $m_{\mathrm{T2}}^{100} \in[100,105)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_100_105>SR-1j $m_{\mathrm{T2}}^{100} \in[100,105)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_105_110>SR-1j $m_{\mathrm{T2}}^{100} \in[105,110)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_105_110>SR-1j $m_{\mathrm{T2}}^{100} \in[105,110)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_110_115>SR-1j $m_{\mathrm{T2}}^{100} \in[110,115)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_110_115>SR-1j $m_{\mathrm{T2}}^{100} \in[110,115)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_115_120>SR-1j $m_{\mathrm{T2}}^{100} \in[115,120)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_115_120>SR-1j $m_{\mathrm{T2}}^{100} \in[115,120)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_120_125>SR-1j $m_{\mathrm{T2}}^{100} \in[120,125)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_125_130>SR-1j $m_{\mathrm{T2}}^{100} \in[125,130)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_130_140>SR-1j $m_{\mathrm{T2}}^{100} \in[130,140)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_130_140>SR-1j $m_{\mathrm{T2}}^{100} \in[130,140)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_140_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[140,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_140_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[140,\infty)$ Efficiency</a> <li><b>Charginos:</b> <a href=?table=Acceptance_SR_DF_81_1_SF_77_1>SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ Acceptance</a> <a href=?table=Efficiency_SR_DF_81_1_SF_77_1>SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ Efficiency</a> <a href=?table=Acceptance_SR_DF_81_1>SR-DF BDT-signal$\in(0.81,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_81_1>SR-DF BDT-signal$\in(0.81,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_82_1>SR-DF BDT-signal$\in(0.82,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_82_1>SR-DF BDT-signal$\in(0.82,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_83_1>SR-DF BDT-signal$\in(0.83,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_83_1>SR-DF BDT-signal$\in(0.83,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_84_1>SR-DF BDT-signal$\in(0.84,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_84_1>SR-DF BDT-signal$\in(0.84,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_85_1>SR-DF BDT-signal$\in(0.85,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_85_1>SR-DF BDT-signal$\in(0.85,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_81_8125>SR-DF BDT-signal$\in(0.81,8125]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_81_8125>SR-DF BDT-signal$\in(0.81,8125]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8125_815>SR-DF BDT-signal$\in(0.8125,815]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8125_815>SR-DF BDT-signal$\in(0.8125,815]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_815_8175>SR-DF BDT-signal$\in(0.815,8175]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_815_8175>SR-DF BDT-signal$\in(0.815,8175]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8175_82>SR-DF BDT-signal$\in(0.8175,82]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8175_82>SR-DF BDT-signal$\in(0.8175,82]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_82_8225>SR-DF BDT-signal$\in(0.82,8225]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_82_8225>SR-DF BDT-signal$\in(0.82,8225]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8225_825>SR-DF BDT-signal$\in(0.8225,825]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8225_825>SR-DF BDT-signal$\in(0.8225,825]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_825_8275>SR-DF BDT-signal$\in(0.825,8275]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_825_8275>SR-DF BDT-signal$\in(0.825,8275]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8275_83>SR-DF BDT-signal$\in(0.8275,83]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8275_83>SR-DF BDT-signal$\in(0.8275,83]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_83_8325>SR-DF BDT-signal$\in(0.83,8325]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_83_8325>SR-DF BDT-signal$\in(0.83,8325]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8325_835>SR-DF BDT-signal$\in(0.8325,835]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8325_835>SR-DF BDT-signal$\in(0.8325,835]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_835_8375>SR-DF BDT-signal$\in(0.835,8375]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_835_8375>SR-DF BDT-signal$\in(0.835,8375]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8375_84>SR-DF BDT-signal$\in(0.8375,84]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8375_84>SR-DF BDT-signal$\in(0.8375,84]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_84_845>SR-DF BDT-signal$\in(0.85,845]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_84_845>SR-DF BDT-signal$\in(0.85,845]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_845_85>SR-DF BDT-signal$\in(0.845,85]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_845_85>SR-DF BDT-signal$\in(0.845,85]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_85_86>SR-DF BDT-signal$\in(0.85,86]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_85_86>SR-DF BDT-signal$\in(0.85,86]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_86_1>SR-DF BDT-signal$\in(0.86,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_86_1>SR-DF BDT-signal$\in(0.86,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_77_1>SR-SF BDT-signal$\in(0.77,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_77_1>SR-SF BDT-signal$\in(0.77,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_78_1>SR-SF BDT-signal$\in(0.78,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_78_1>SR-SF BDT-signal$\in(0.78,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_79_1>SR-SF BDT-signal$\in(0.79,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_79_1>SR-SF BDT-signal$\in(0.79,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_80_1>SR-SF BDT-signal$\in(0.80,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_80_1>SR-SF BDT-signal$\in(0.80,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_77_775>SR-SF BDT-signal$\in(0.77,0.775]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_77_775>SR-SF BDT-signal$\in(0.77,0.775]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_775_78>SR-SF BDT-signal$\in(0.775,0.78]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_775_78>SR-SF BDT-signal$\in(0.775,0.78]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_78_785>SR-SF BDT-signal$\in(0.78,0.785]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_78_785>SR-SF BDT-signal$\in(0.78,0.785]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_785_79>SR-SF BDT-signal$\in(0.785,0.79]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_785_79>SR-SF BDT-signal$\in(0.785,0.79]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_79_795>SR-SF BDT-signal$\in(0.79,0.795]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_79_795>SR-SF BDT-signal$\in(0.79,0.795]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_795_80>SR-SF BDT-signal$\in(0.795,0.80]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_795_80>SR-SF BDT-signal$\in(0.795,0.80]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_80_81>SR-SF BDT-signal$\in(0.80,0.81]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_80_81>SR-SF BDT-signal$\in(0.80,0.81]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_81_1>SR-SF BDT-signal$\in(0.81,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_81_1>SR-SF BDT-signal$\in(0.81,1]$ Efficiency</a></ul> <b>Truth Code snippets</b>, <b>SLHA</b> and <b>machine learning</b> files are available under "Resources" (purple button on the left)
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-0J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.
Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-1J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.
Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.
The upper panel shows the observed number of events in each of the binned SRs defined in Table 3, together with the expected SM backgrounds obtained after applying the efficiency correction method to compute the number of expected FSB events. `Others' include the non-dominant background sources, e.g. $t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. The distributions of two signal points with mass splittings $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 30$ GeV and $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 50$ GeV are overlaid. The lower panel shows the significance as defined in Ref. [115].
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,0.795]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,0.795]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.795,0.80]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.795,0.80]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,0.81]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,0.81]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
Cutflow table for the chargino signal sample with $m\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0=(125,25)$ GeV, in the SR-SF BDT-signal$\in (0.77,1]$ and SR-DF BDT-signal$\in (0.81,1]$ regions. The yields include the process cross-section and are weighted to the 139 fb$^{-1}$ luminosity. 170000 events were generated for the sample.
Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for chargino-pair production with $W$-boson-mediated decays in the $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ plane. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
The upper panel shows the observed number of events in the SRs defined in Table 3, together with the expected SM backgrounds obtained after the background fit in the CRs. `Others' include the non-dominant background sources, e.g.$t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. Distributions for three benchmark signal points are overlaid for comparison. The lower panel shows the significance as defined in Ref. [115].
This paper presents a measurement of fiducial and differential cross-sections for $W^{+}W^{-}$ production in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS experiment at the Large Hadron Collider using a dataset corresponding to an integrated luminosity of 139 fb$^{-1}$. Events with exactly one electron, one muon and no hadronic jets are studied. The fiducial region in which the measurements are performed is inspired by searches for the electroweak production of supersymmetric charginos decaying to two-lepton final states. The selected events have moderate values of missing transverse momentum and the `stransverse mass' variable $m_{\textrm{T2}}$, which is widely used in searches for supersymmetry at the LHC. The ranges of these variables are chosen so that the acceptance is enhanced for direct $W^{+}W^{-}$ production and suppressed for production via top quarks, which is treated as a background. The fiducial cross-section and particle-level differential cross-sections for six variables are measured and compared with two theoretical SM predictions from perturbative QCD calculations.
Signal region detector-level distribution for the observable $|y_{e\mu}|$.
Signal region detector-level distribution for the observable $|\Delta \phi(e \mu)|$.
Signal region detector-level distribution for the observable $ \cos\theta^{\ast}$.
Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$.
Signal region detector-level distribution for the observable $m_{e\mu}$.
Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{e\mu}$.
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$
A search for new phenomena has been performed in final states with at least one isolated high-momentum photon, jets and missing transverse momentum in proton--proton collisions at a centre-of-mass energy of $\sqrt{s} = 13$ TeV. The data, collected by the ATLAS experiment at the CERN LHC, correspond to an integrated luminosity of 139 $fb^{-1}$. The experimental results are interpreted in a supersymmetric model in which pair-produced gluinos decay into neutralinos, which in turn decay into a gravitino, at least one photon, and jets. No significant deviations from the predictions of the Standard Model are observed. Upper limits are set on the visible cross section due to physics beyond the Standard Model, and lower limits are set on the masses of the gluinos and neutralinos, all at 95% confidence level. Visible cross sections greater than 0.022 fb are excluded and pair-produced gluinos with masses up to 2200 GeV are excluded for most of the NLSP masses investigated.
This Letter presents a search for direct production of charginos and neutralinos via electroweak interactions. The results are based on data from proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 137 fb$^{-1}$. The search considers final states with large missing transverse momentum and pairs of hadronically decaying bosons WW, WZ, and WH, where H is the Higgs boson. These bosons are identified using novel algorithms. No significant excess of events is observed relative to the expectations from the standard model. Limits at the 95% confidence level are placed on the cross section for production of mass-degenerate wino-like supersymmetric particles $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$, and mass-degenerate higgsino-like supersymmetric particles $\tilde{\chi}_1^\pm$, $\tilde{\chi}_2^0$, and $\tilde{\chi}_3^0$. In the limit of a nearly-massless lightest supersymmetric particle $\tilde{\chi}_1^0$, wino-like particles with masses up to 870 and 960 GeV are excluded in the cases of $\tilde{\chi}_2^0$ $\to$ Z$\tilde{\chi}_1^0$ and $\tilde{\chi}_2^0$ $\to$ H$\tilde{\chi}_1^0$, respectively, and higgsino-like particles are excluded between 300 and 650 GeV.
This paper presents a search for hypothetical massive, charged, long-lived particles with the ATLAS detector at the LHC using an integrated luminosity of 139 fb$^{-1}$ of proton-proton collisions at $\sqrt{s}=13$ TeV. These particles are expected to move significantly slower than the speed of light and should be identifiable by their high transverse momenta and anomalously large specific ionisation losses, ${\mathrm{d}}E/\mathrm{d}x$. Trajectories reconstructed solely by the inner tracking system and a ${\mathrm{d}}E/\mathrm{d}x$ measurement in the pixel detector layers provide sensitivity to particles with lifetimes down to ${\cal O}(1)$$\text{ns}$ with a mass, measured using the Bethe--Bloch relation, ranging from 100 GeV to 3 TeV. Interpretations for pair-production of $R$-hadrons, charginos and staus in scenarios of supersymmetry compatible with these particles being long-lived are presented, with mass limits extending considerably beyond those from previous searches in broad ranges of lifetime.
This material aims to give people outside the ATLAS Collaboration the possibility to reinterpret the results from the search for heavy charged long-lived particles (CLLPs), using only particles from Monte Carlo event generators. The reinterpretation material is provided for signal regions SR-Inclusive_Low and SR-Inclusive_High. <ul display="inline-block"> <li>The "long" lifetime regime of mass windows is used.</li> <li>Users are guided to read Guide.pdf (available from "Resources" or "Download All" buttons) for how to use the provided materials for reinterpretation.</li> <li>The pseudo-code snippet snippet.cxx also illustrates a sketch of possible implementation.</li> </ul> <b>Signal Region (Discovery) mass distribution</b> <ul> <li><a href="?table=SR-Inclusive_Low%20mass%20distribution">SR-Inclusive_Low mass distribution</a></li> <li><a href="?table=SR-Inclusive_High%20mass%20distribution">SR-Inclusive_High mass distribution</a></li> </ul> <b>Signal Region (Discovery) $p_\text{T}, \eta, dE/dx$ distribution</b> <ul> <li><a href="?table=SR-Inclusive_Low%20pT%20distribution">SR-Inclusive_Low pT distribution</a></li> <li><a href="?table=SR-Inclusive_High%20pT%20distribution">SR-Inclusive_High pT distribution</a></li> <li><a href="?table=SR-Inclusive_Low%20$eta$%20distribution">SR-Inclusive_Low $\eta$ distribution</a></li> <li><a href="?table=SR-Inclusive_High%20$eta$%20distribution">SR-Inclusive_High $\eta$ distribution</a></li> <li><a href="?table=SR-Inclusive_Low%20dE/dx%20distribution">SR-Inclusive_Low dE/dx distribution</a></li> <li><a href="?table=SR-Inclusive_High%20dE/dx%20distribution">SR-Inclusive_High dE/dx distribution</a></li> </ul> <b>Signal Region (Limit Setting) mass distribution</b> <ul> <li><a href="?table=SR-Trk-IBL0_Low%20mass%20distribution">SR-Trk-IBL0_Low mass distribution</a></li> <li><a href="?table=SR-Mu-IBL0_Low%20mass%20distribution">SR-Mu-IBL0_Low mass distribution</a></li> <li><a href="?table=SR-Trk-IBL0_High%20mass%20distribution">SR-Trk-IBL0_High mass distribution</a></li> <li><a href="?table=SR-Mu-IBL0_High%20mass%20distribution">SR-Mu-IBL0_High mass distribution</a></li> <li><a href="?table=SR-Trk-IBL1%20mass%20distribution">SR-Trk-IBL1 mass distribution</a></li> <li><a href="?table=SR-Mu-IBL1%20mass%20distribution">SR-Mu-IBL1 mass distribution</a></li> </ul> <b>Signal Region (Limit Setting) $p_\text{T}$ distribution</b> <ul> <li><a href="?table=SR-Trk-IBL0_Low%20pT%20distribution">SR-Trk-IBL0_Low pT distribution</a></li> <li><a href="?table=SR-Mu-IBL0_Low%20pT%20distribution">SR-Mu-IBL0_Low pT distribution</a></li> <li><a href="?table=SR-Trk-IBL0_High%20pT%20distribution">SR-Trk-IBL0_High pT distribution</a></li> <li><a href="?table=SR-Mu-IBL0_High%20pT%20distribution">SR-Mu-IBL0_High pT distribution</a></li> <li><a href="?table=SR-Trk-IBL1%20pT%20distribution">SR-Trk-IBL1 pT distribution</a></li> <li><a href="?table=SR-Mu-IBL1%20pT%20distribution">SR-Mu-IBL1 pT distribution</a></li> </ul> <b>Signal Region (Limit Setting) $dE/dx$ distribution</b> <ul> <li><a href="?table=SR-Trk-IBL0_Low%20dE/dx%20distribution">SR-Trk-IBL0_Low dE/dx distribution</a></li> <li><a href="?table=SR-Mu-IBL0_Low%20dE/dx%20distribution">SR-Mu-IBL0_Low dE/dx distribution</a></li> <li><a href="?table=SR-Trk-IBL0_High%20dE/dx%20distribution">SR-Trk-IBL0_High dE/dx distribution</a></li> <li><a href="?table=SR-Mu-IBL0_High%20dE/dx%20distribution">SR-Mu-IBL0_High dE/dx distribution</a></li> <li><a href="?table=SR-Trk-IBL1%20dE/dx%20distribution">SR-Trk-IBL1 dE/dx distribution</a></li> <li><a href="?table=SR-Mu-IBL1%20dE/dx%20distribution">SR-Mu-IBL1 dE/dx distribution</a></li> </ul> <b>Discovery Signal Regions $p_{0}$ values</b> <ul> <li><a href="?table=p0-values%20and%20model-independent%20limits,%20short%20regime">p0-values and model-independent limits, short regime</a></li> <li><a href="?table=p0-values%20and%20model-independent%20limits,%20long%20regime">p0-values and model-independent limits, long regime</a></li> </ul> <b>Validation Region plots</b> <ul> <li><a href="?table=VR-LowPt-Inclusive_High%20mass%20distribution">VR-LowPt-Inclusive_High mass distribution</a></li> <li><a href="?table=VR-HiEta-Inclusive%20mass%20distribution">VR-HiEta-Inclusive mass distribution</a></li> </ul> <ul> <li><a href="?table=VR-LowPt-Trk-IBL0_Low%20mass%20distribution">VR-LowPt-Trk-IBL0_Low mass distribution</a></li> <li><a href="?table=VR-LowPt-Mu-IBL0_Low%20mass%20distribution">VR-LowPt-Mu-IBL0_Low mass distribution</a></li> <li><a href="?table=VR-LowPt-Trk-IBL0_High%20mass%20distribution">VR-LowPt-Trk-IBL0_High mass distribution</a></li> <li><a href="?table=VR-LowPt-Mu-IBL0_High%20mass%20distribution">VR-LowPt-Mu-IBL0_High mass distribution</a></li> <li><a href="?table=VR-LowPt-Trk-IBL1%20mass%20distribution">VR-LowPt-Trk-IBL1 mass distribution</a></li> <li><a href="?table=VR-LowPt-Mu-IBL1%20mass%20distribution">VR-LowPt-Mu-IBL1 mass distribution</a></li> </ul> <ul> <li><a href="?table=VR-HiEta-Trk-IBL0_Low%20mass%20distribution">VR-HiEta-Trk-IBL0_Low mass distribution</a></li> <li><a href="?table=VR-HiEta-Mu-IBL0_Low%20mass%20distribution">VR-HiEta-Mu-IBL0_Low mass distribution</a></li> <li><a href="?table=VR-HiEta-Trk-IBL0_High%20mass%20distribution">VR-HiEta-Trk-IBL0_High mass distribution</a></li> <li><a href="?table=VR-HiEta-Mu-IBL0_High%20mass%20distribution">VR-HiEta-Mu-IBL0_High mass distribution</a></li> <li><a href="?table=VR-HiEta-Trk-IBL1%20mass%20distribution">VR-HiEta-Trk-IBL1 mass distribution</a></li> <li><a href="?table=VR-HiEta-Mu-IBL1%20mass%20distribution">VR-HiEta-Mu-IBL1 mass distribution</a></li> </ul> <b>Mass vs. Lifetime limit plots</b> <ul> <li><a href="?table=Mass%20Limit%20vs.%20Lifetime,%20R-hadron,%20Expected">Mass Limit vs. Lifetime, R-hadron, Expected</a></li> <li><a href="?table=Mass%20Limit%20vs.%20Lifetime,%20R-hadron,%20Observed">Mass Limit vs. Lifetime, R-hadron, Observed</a></li> <li><a href="?table=Mass%20Limit%20vs.%20Lifetime,%20R-hadron,%20compressed,%20Expected">Mass Limit vs. Lifetime, R-hadron, compressed, Expected</a></li> <li><a href="?table=Mass%20Limit%20vs.%20Lifetime,%20R-hadron,%20compressed,%20Observed">Mass Limit vs. Lifetime, R-hadron, compressed, Observed</a></li> <li><a href="?table=Mass%20Limit%20vs.%20Lifetime,%20Chargino,%20Expected">Mass Limit vs. Lifetime, Chargino, Expected</a></li> <li><a href="?table=Mass%20Limit%20vs.%20Lifetime,%20Chargino,%20Observed">Mass Limit vs. Lifetime, Chargino, Observed</a></li> <li><a href="?table=Mass%20Limit%20vs.%20Lifetime,%20Stau,%20Expected">Mass Limit vs. Lifetime, Stau, Expected</a></li> <li><a href="?table=Mass%20Limit%20vs.%20Lifetime,%20Stau,%20Observed">Mass Limit vs. Lifetime, Stau, Observed</a></li> </ul> <b>Cross-section limit plots</b> <ul> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%201ns">Cross Section Limit, R-hadron 1ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%203ns">Cross Section Limit, R-hadron 3ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%2010ns">Cross Section Limit, R-hadron 10ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%2030ns">Cross Section Limit, R-hadron 30ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%20Stable">Cross Section Limit, R-hadron Stable</a></li> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%20Compressed%201ns">Cross Section Limit, R-hadron Compressed 1ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%20Compressed%203ns">Cross Section Limit, R-hadron Compressed 3ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%20Compressed%2010ns">Cross Section Limit, R-hadron Compressed 10ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20R-hadron%20Compressed%2030ns">Cross Section Limit, R-hadron Compressed 30ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Chargino%201ns">Cross Section Limit, Chargino 1ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Chargino%204ns">Cross Section Limit, Chargino 4ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Chargino%2010ns">Cross Section Limit, Chargino 10ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Chargino%2030ns">Cross Section Limit, Chargino 30ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Chargino%20Stable">Cross Section Limit, Chargino Stable</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Stau%201ns">Cross Section Limit, Stau 1ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Stau%203ns">Cross Section Limit, Stau 3ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Stau%2010ns">Cross Section Limit, Stau 10ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Stau%2030ns">Cross Section Limit, Stau 30ns</a></li> <li><a href="?table=Cross%20Section%20Limit,%20Stau%20Stable">Cross Section Limit, Stau Stable</a></li> </ul> <b>Signal Region events projected to other kinematic variables</b> <ul> <li><a href="?table=SR-Inclusive_Low%20MET">SR-Inclusive_Low MET</a></li> <li><a href="?table=SR-Inclusive_High%20MET">SR-Inclusive_High MET</a></li> <li><a href="?table=SR-Inclusive_Low%20deltaPhi(MET,%20Track)">SR-Inclusive_Low deltaPhi(MET, Track)</a></li> <li><a href="?table=SR-Inclusive_High%20deltaPhi(MET,%20Track)">SR-Inclusive_High deltaPhi(MET, Track)</a></li> <li><a href="?table=SR-Inclusive_Low%20mT(MET,%20Track)">SR-Inclusive_Low mT(MET, Track)</a></li> <li><a href="?table=SR-Inclusive_High%20mT(MET,%20Track)">SR-Inclusive_High mT(MET, Track)</a></li> <li><a href="?table=SR-Inclusive_Low%20Leading%20jet%20pT">SR-Inclusive_Low Leading jet pT</a></li> <li><a href="?table=SR-Inclusive_High%20Leading%20jet%20pT">SR-Inclusive_High Leading jet pT</a></li> <li><a href="?table=SR-Inclusive_Low%20deltaPhi(Leading%20jet,%20Track)">SR-Inclusive_Low deltaPhi(Leading jet, Track)</a></li> <li><a href="?table=SR-Inclusive_High%20deltaPhi(Leading%20jet,%20Track)">SR-Inclusive_High deltaPhi(Leading jet, Track)</a></li> <li><a href="?table=SR-Inclusive_Low%20deltaPhi(MET,%20Leading%20jet)">SR-Inclusive_Low deltaPhi(MET, Leading jet)</a></li> <li><a href="?table=SR-Inclusive_High%20deltaPhi(MET,%20Leading%20jet)">SR-Inclusive_High deltaPhi(MET, Leading jet)</a></li> <li><a href="?table=SR-Inclusive_Low%20mT(MET,%20Leading%20jet)">SR-Inclusive_Low mT(MET, Leading jet)</a></li> <li><a href="?table=SR-Inclusive_High%20mT(MET,%20Leading%20jet)">SR-Inclusive_High mT(MET, Leading jet)</a></li> <li><a href="?table=SR-Inclusive_Low%20Effective%20mass">SR-Inclusive_Low Effective mass</a></li> <li><a href="?table=SR-Inclusive_High%20Effective%20mass">SR-Inclusive_High Effective mass</a></li> </ul> <b>Acceptance and efficiency values for reinterpretation</b> <ul> <li><a href="?table=Muon%20Reconstruction%20Efficiency%20distribution">Muon Reconstruction Efficiency distribution</a></li> <li><a href="?table=Muon%20Reconstruction%20Efficiency,%20R-hadron%20distribution">Muon Reconstruction Efficiency, R-hadron distribution</a></li> <li><a href="?table=Trigger%20Efficiency%20distribution">Trigger Efficiency distribution</a></li> <li><a href="?table=Event%20Selection%20Efficiency%20distribution">Event Selection Efficiency distribution</a></li> <li><a href="?table=Track%20Selection%20Efficiency%20distribution">Track Selection Efficiency distribution</a></li> <li><a href="?table=Mass%20Window%20Efficiency">Mass Window Efficiency</a></li> </ul> <b>Acceptance and efficiency tables for signal samples</b> <ul> <li><a href="?table=Acceptance,%20R-hadron">Acceptance, R-hadron</a></li> <li><a href="?table=Acceptance,%20R-hadron,%20compressed">Acceptance, R-hadron, compressed</a></li> <li><a href="?table=Acceptance,%20Chargino">Acceptance, Chargino</a></li> <li><a href="?table=Acceptance,%20Stau">Acceptance, Stau</a></li> </ul> <ul> <li><a href="?table=Event-level%20efficiency,%20R-hadron">Event-level efficiency, R-hadron</a></li> <li><a href="?table=Event-level%20efficiency,%20R-hadron,%20compressed">Event-level efficiency, R-hadron, compressed</a></li> <li><a href="?table=Event-level%20efficiency,%20Chargino">Event-level efficiency, Chargino</a></li> <li><a href="?table=Event-level%20efficiency,%20Stau">Event-level efficiency, Stau</a></li> </ul> <ul> <li><a href="?table=Efficiency,%20SR-Inclusve_High,%20R-hadron">Efficiency, SR-Inclusve_High, R-hadron</a></li> <li><a href="?table=Efficiency,%20SR-Inclusve_High,%20R-hadron,%20compressed">Efficiency, SR-Inclusve_High, R-hadron, compressed</a></li> <li><a href="?table=Efficiency,%20SR-Inclusve_High,%20Chargino">Efficiency, SR-Inclusve_High, Chargino</a></li> <li><a href="?table=Efficiency,%20SR-Inclusve_High,%20Stau">Efficiency, SR-Inclusve_High, Stau</a></li> </ul> <ul> <li><a href="?table=Efficiency,%20SR-Inclusive_Low,%20R-hadron">Efficiency, SR-Inclusive_Low, R-hadron</a></li> <li><a href="?table=Efficiency,%20SR-Inclusive_Low,%20R-hadron,%20compressed">Efficiency, SR-Inclusive_Low, R-hadron, compressed</a></li> <li><a href="?table=Efficiency,%20SR-Inclusive_Low,%20Chargino">Efficiency, SR-Inclusive_Low, Chargino</a></li> <li><a href="?table=Efficiency,%20SR-Inclusive_Low,%20Stau">Efficiency, SR-Inclusive_Low, Stau</a></li> </ul> <b>Cut flow for signal samples</b> <ul> <li><a href="?table=Cut%20Flow,%20R-hadron">Cut Flow, R-hadron</a></li> <li><a href="?table=Cut%20Flow,%20R-hadron,%20compressed">Cut Flow, R-hadron, compressed</a></li> <li><a href="?table=Cut%20Flow,%20Chargino">Cut Flow, Chargino</a></li> <li><a href="?table=Cut%20Flow,%20Stau">Cut Flow, Stau</a></li> </ul>
Comparison of the observed and expected VAR distributionsin VR-LowPt-Inclusive_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-HiEta-Inclusive. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
The observed mass distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed mass distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
List of expected and observed events, $p_{0}$-value and the corresponding $Z$ local significance, as well as the 95% CLs upper limit of the expected and observed signal events ($S^{95}_ ext{exp} and $S^{95}_ ext{obs}$ ) in each mass window for SR-Inclusive bins of the short lifetime regime.
List of expected and observed events, $p_{0}$-value and the corresponding $Z$ local significance, as well as the 95% CLs upper limit of the expected and observed signal events ($S^{95}_ ext{exp} and $S^{95}_ ext{obs}$ ) in each mass window for SR-Inclusive bins of the long lifetime regime.
The observed $p_{\rm T$ distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed $p_{\rm T$ distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed $|\eta|$ distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed $|\eta|$ distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed dE/dx distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed dE/dx distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed mass distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed mass distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed mass distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed mass distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed mass distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed mass distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.
Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.
Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.
Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.
(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.
(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.
(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.
(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.
Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.
The observed $p_{\rm T$ distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed $p_{\rm T$ distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed $p_{\rm T$ distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed $p_{\rm T$ distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed $p_{\rm T$ distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed $p_{\rm T$ distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed dE/dx distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed dE/dx distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed dE/dx distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed dE/dx distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed dE/dx distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
The observed dE/dx distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.
Expected and observed distributions in SR-Inclusive_Low of missing transverse momentum. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_High of missing transverse momentum. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_Low of relative phi-angle between pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_High of relative phi-angle between pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_Low of the transverse mass of pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_High of the transverse mass of pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_Low of the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_High of the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_Low of the relative phi-angle between the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track, and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_High of the relative phi-angle between the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track, and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_Low of the relative phi-angle between pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_High of the relative phi-angle between pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_Low of the transverse mass of pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_High of the transverse mass of pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_Low of the effective mass, defined as the scalar sum pT of the signal candidate track, jets satisfying pT > 30 GeV, excluding ones within deltaR < 0.4 with respect to the signal candidate track, and pTmiss. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
Expected and observed distributions in SR-Inclusive_High of the effective mass, defined as the scalar sum pT of the signal candidate track, jets satisfying pT > 30 GeV, excluding ones within deltaR < 0.4 with respect to the signal candidate track, and pTmiss. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.
The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.
The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.
The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.
The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.
The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.
Muon reconstruction efficiency as a function of β and |η| for (a) stable charginos and (b) stable charged R-hadrons. For weakly interacting LLPs with calorimeter materials the efficiency for the chargino is recommended to refer to. The muon reconstruction efficiency for R-hadrons is significantly lower due to having QCD interactions with materials.
Muon reconstruction efficiency as a function of β and |η| for (a) stable charginos and (b) stable charged R-hadrons. For weakly interacting LLPs with calorimeter materials the efficiency for the chargino is recommended to refer to. The muon reconstruction efficiency for R-hadrons is significantly lower due to having QCD interactions with materials.
Trigger and event selection efficiencies. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.
Trigger and event selection efficiencies. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.
Signal track selection efficiency as a function of CLLP $\beta\gamma$ for SR-Inclusive_Low and SR-Inclusive_High bins. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.
Signal selection efficiency by the mass window for SR-Inclusive_Low and SR-Inclusive_High bins.
Acceptance for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.
Acceptance for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |eta| < 1.8 and r_decay > 500 mm.
Acceptance for the chargino pair-production model for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.
Acceptance for the stau pair-production model for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.
Event-level efficiency for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.
Event-level efficiency for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.
Event-level efficiency for the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.
Event-level efficiency for the stau pair-production model for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.
Efficiency of SR-Inclusive_Highfor the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.
Efficiency of SR-Inclusive_Highfor the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.
Efficiency of SR-Inclusive_Highfor the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.
Efficiency of SR-Inclusive_Highfor the stau pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.
Efficiency of SR-Inclusive_Low for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.
Efficiency of SR-Inclusive_Low for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.
Efficiency of SR-Inclusive_Low for the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.
Efficiency of SR-Inclusive_Low for the stau pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.
Passing events in event selection steps for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes.
Passing events in event selection steps for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes.
Passing events in event selection steps for the chargino pair-production model for various masses and lifetimes.
Passing events in event selection steps for the stau pair-production model for various masses and lifetimes.
Searches for new phenomena inspired by supersymmetry in final states containing an $e^+e^-$ or $\mu^+\mu^-$ pair, jets, and missing transverse momentum are presented. These searches make use of proton-proton collision data with an integrated luminosity of 139 $\text{fb}^{-1}$, collected during 2015-2018 at a centre-of-mass energy $\sqrt{s}=13 $TeV by the ATLAS detector at the Large Hadron Collider. Two searches target the pair production of charginos and neutralinos. One uses the recursive-jigsaw reconstruction technique to follow up on excesses observed in 36.1 $\text{fb}^{-1}$ of data, and the other uses conventional event variables. The third search targets pair production of coloured supersymmetric particles (squarks or gluinos) decaying through the next-to-lightest neutralino $(\tilde\chi_2^0)$ via a slepton $(\tilde\ell)$ or $Z$ boson into $\ell^+\ell^-\tilde\chi_1^0$, resulting in a kinematic endpoint or peak in the dilepton invariant mass spectrum. The data are found to be consistent with the Standard Model expectations. Results are interpreted using simplified models and exclude masses up to 900 GeV for electroweakinos, 1550 GeV for squarks, and 2250 GeV for gluinos.
- - - - - - - - Overview of HEPData Record - - - - - - - - <br/><br/> <b>EWK SR distributions:</b> <a href="116034?version=1&table=Figure 11a">SR-High_8-EWK</a>; <a href="116034?version=1&table=Figure 11b">SR-ℓℓ𝑏𝑏-EWK</a>; <a href="116034?version=1&table=Figure 11c">SR-Int-EWK</a>; <a href="116034?version=1&table=Figure 11d">SR-Low-EWK</a>; <a href="116034?version=1&table=Figure 11e">SR-OffShell-EWK</a><br/><br/> <b>Strong SR distributions:</b> <a href="116034?version=1&table=Figure 13a">SRC-STR</a>; <a href="116034?version=1&table=Figure 13b">SRLow-STR</a>; <a href="116034?version=1&table=Figure 13c">SRMed-STR</a>; <a href="116034?version=1&table=Figure 13d">SRHigh-STR</a><br/><br/> <b>RJR SR Yields:</b> <a href="116034?version=1&table=Table 16">SR2l-Low-RJR, SR2l-ISR-RJR</a><br/><br/> <b>EWK SR Yields:</b> <a href="116034?version=1&table=Table 18">SR-High_16a-EWK, SR-High_8a-EWK, SR-1J-High-EWK, SR-ℓℓ𝑏𝑏-EWK, SR-High_16b-EWK, SR-High_8b-EWK</a>; <a href="116034?version=1&table=Table 19">SR-Int_a-EWK, SR-Low_a-EWK, SR-Low-2-EWK, SR-OffShell_a-EWK, SR-Int_b-EWK, SR-Low_b-EWK, SR-OffShell_b-EWK </a><br/><br/> <b>Strong SR Yields:</b> <a href="116034?version=1&table=Table 21">SRC-STR, SRLow-STR, SRMed-STR, SRHigh-STR</a>; <a href="116034?version=1&table=Table 22">SRZLow-STR, SRZMed-STR, SRZHigh-STR</a><br/><br/> <b>C1N2 Model Limits:</b> <a href="116034?version=1&table=Table 15a C1N2 Observed Limit">Obs</a>; <a href="116034?version=1&table=Table 15a C1N2 Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 34a C1N2 Expected XS Upper Limit">Upper Limits</a><br/><br/> <b>GMSB Model Limits:</b> <a href="116034?version=1&table=Table 15b GMSB Observed Limit">Obs</a>; <a href="116034?version=1&table=Table 15b GMSB Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 34b GMSB Expected XS Upper Limit">Upper Limits</a><br/><br/> <b>Gluon-Slepton Model Limits:</b> <a href="116034?version=1&table=Figure 16a Observed Limit">Obs</a>; <a href="116034?version=1&table=Figure 16a Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 23a XS Upper Limit">Upper Limits</a><br/><br/> <b>Gluon-Z* Model Limits:</b> <a href="116034?version=1&table=Figure 16b Observed Limit">Obs</a>; <a href="116034?version=1&table=Figure 16b Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 23b XS Upper Limit">Upper Limits</a><br/><br/> <b>Squark-Z* Model Limits:</b> <a href="116034?version=1&table=Figure 16c Observed Limit">Obs</a>; <a href="116034?version=1&table=Figure 16c Expected Limit">Exp</a>; <a href="116034?version=1&table=Auxiliary Figure 23c XS Upper Limit">Upper Limits</a><br/><br/> <b>EWK VR distributions:</b> <a href="116034?version=1&table=Figure 4a S_ETmiss in VR-High-Sideband-EWK">VR-High-Sideband-EWK</a>; <a href="116034?version=1&table=Figure 4b S_Etmiss in VR-High-R-EWK">VR-High-R-EWK</a>; <a href="116034?version=1&table=Figure 4c S_Etmiss in VR-1J-High-EWK">VR-1J-High-EWK</a>; <a href="116034?version=1&table=Figure 4d S_Etmiss in VR-llbb-EWK">VR-ℓℓ𝑏𝑏-EWK</a>; <a href="116034?version=1&table=Figure 5a S_Etmiss in VR-Int-EWK">VR-Int-EWK</a>; <a href="116034?version=1&table=Figure 5b S_Etmiss in VR-Low-EWK">VR-Low-EWK</a>; <a href="116034?version=1&table=Figure 5c S_Etmiss in VR-Low-2-EWK">VR-Low-2-EWK</a>; <a href="116034?version=1&table=Figure 5d S_Etmiss in VR-OffShell-EWK">VR-OffShell-EWK</a><br/><br/> <b>Strong VR distributions:</b> <a href="116034?version=1&table=Figure 6a">VRC-STR</a>; <a href="116034?version=1&table=Figure 6b">VRLow-STR</a>; <a href="116034?version=1&table=Figure 6c">VRMed-STR</a>; <a href="116034?version=1&table=Figure 6d">VRHigh-STR</a>; <a href="116034?version=1&table=Figure 8">VR3L-STR</a><br/><br/> <b>Other Strong distributions:</b> <a href="116034?version=1&table=Auxiliary Figure 17a">SRLow-STR + VRLow-STR</a><br/><br/> <b>Other EWK distributions:</b> <a href="116034?version=1&table=Auxiliary Figure 33a Mjj in CR-Z-EWK and SR-Low-EWK">CR-Z-EWK + SR-Low-EWK</a>; <a href="116034?version=1&table=Auxiliary Figure 33b S_ETmiss in CR-Z-met-EWK">CR-Z-met-EWK</a><br/><br/> <b>Strong Signal Cutflows:</b> <a href="116034?version=1&table=Auxiliary Table 30-31 SRC-STR Cutflow">SRC-STR GG_N2_ZN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRMed-STR Cutflow">SRC-STR SS_N2_ZN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRLow-STR Cutflow">SRLow-STR GG_N2_SLN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRHigh-STR Cutflow">SRC-STR GG_N2_SLN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRZLow-STR Cutflow">SRZLow-STR SS_N2_ZN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRZMed-STR Cutflow">SRZMed-STR SS_N2_ZN1</a>; <a href="116034?version=1&table=Auxiliary Table 30-31 SRZHigh-STR Cutflow">SRZHigh-STR SS_N2_ZN1</a><br/><br/> <b>EWK Signal Cutflows:</b> <a href="116034?version=1&table=Auxiliary Table 36 SR-OffShell_a-EWK Cutflow"> SR-OffShell_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 37 SR-OffShell_b-EWK Cutflow"> SR-OffShell_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 38 SR-Low_a-EWK Cutflow"> SR-Low_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 39 SR-Low_b-EWK Cutflow"> SR-Low_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 40 SR-Low-2-EWK Cutflow"> SR-Low-2-E</a>; <a href="116034?version=1&table=Auxiliary Table 41 SR-Int_a-EWK Cutflow"> SR-Int_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 42 SR-Int_b-EWK Cutflow"> SR-Int_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 43 SR-High_16a-EWK Cutflow"> SR-High_16a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 44 SR-High_16b-EWK Cutflow"> SR-High_16b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 45 SR-High_8a-EWK Cutflow"> SR-High_8a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 46 SR-High_8b-EWK Cutflow"> SR-High_8b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 47 SR-1J-High-EWK Cutflow"> SR-1J-Hig</a>; <a href="116034?version=1&table=Auxiliary Table 48 SR-llbb-EWK Cutflow"> SR-llbb-EWK</a><br/><br/> <b>EWK Signal Number of MC Events:</b> <a href="116034?version=1&table=Auxiliary Table 36 SR-OffShell_a-EWK Generated"> SR-OffShell_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 37 SR-OffShell_b-EWK Generated"> SR-OffShell_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 38 SR-Low_a-EWK Generated"> SR-Low_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 39 SR-Low_b-EWK Generated"> SR-Low_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 40 SR-Low-2-EWK Generated"> SR-Low-2-E</a>; <a href="116034?version=1&table=Auxiliary Table 41 SR-Int_a-EWK Generated"> SR-Int_a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 42 SR-Int_b-EWK Generated"> SR-Int_b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 43 SR-High_16a-EWK Generated"> SR-High_16a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 44 SR-High_16b-EWK Generated"> SR-High_16b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 45 SR-High_8a-EWK Generated"> SR-High_8a-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 46 SR-High_8b-EWK Generated"> SR-High_8b-EWK</a>; <a href="116034?version=1&table=Auxiliary Table 47 SR-1J-High-EWK Generated"> SR-1J-Hig</a>; <a href="116034?version=1&table=Auxiliary Table 48 SR-llbb-EWK Generated"> SR-llbb-EWK</a><br/><br/> <b>SRC-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_SLN1 acc in SRC">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 acc in SRC">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRC">SS_N2_ZN1</a><br/><br/> <b>SRLow-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_SLN1 acc in SRLow">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 acc in SRLow">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRLow">SS_N2_ZN1</a><br/><br/> <b>SRMed-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_SLN1 acc in SRMed">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 acc in SRMed">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRMed">SS_N2_ZN1</a><br/><br/> <b>SRHigh-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_SLN1 acc in SRHigh">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 acc in SRHigh">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRHigh">SS_N2_ZN1</a><br/><br/> <b>SRZLow-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_ZN1 acc in SRZLow">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRZLow">SS_N2_ZN1</a><br/><br/> <b>SRZMed-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_ZN1 acc in SRZMed">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRZMed">SS_N2_ZN1</a><br/><br/> <b>SRZHigh-STR Signal Acceptance:</b> <a href="116034?version=1&table=GG_N2_ZN1 acc in SRZHigh">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 acc in SRZHigh">SS_N2_ZN1</a><br/><br/> <b>SRC-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_SLN1 eff in SRC">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 eff in SRC">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRC">SS_N2_ZN1</a><br/><br/> <b>SRLow-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_SLN1 eff in SRLow">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 eff in SRLow">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRLow">SS_N2_ZN1</a><br/><br/> <b>SRMed-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_SLN1 eff in SRMed">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 eff in SRMed">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRMed">SS_N2_ZN1</a><br/><br/> <b>SRHigh-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_SLN1 eff in SRHigh">GG_N2_SLN1</a>; <a href="116034?version=1&table=GG_N2_ZN1 eff in SRHigh">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRHigh">SS_N2_ZN1</a><br/><br/> <b>SRZLow-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_ZN1 eff in SRZLow">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRZLow">SS_N2_ZN1</a><br/><br/> <b>SRZMed-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_ZN1 eff in SRZMed">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRZMed">SS_N2_ZN1</a><br/><br/> <b>SRZHigh-STR Signal Efficiency:</b> <a href="116034?version=1&table=GG_N2_ZN1 eff in SRZHigh">GG_N2_ZN1</a>; <a href="116034?version=1&table=SS_N2_ZN1 eff in SRZHigh">SS_N2_ZN1</a><br/><br/> <b>SR-OffShell_a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-OffShell_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-OffShell_a-EWK">C1N2</a>; <br/><br/> <b>SR-OffShell_b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-OffShell_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-OffShell_b-EWK">C1N2</a>; <br/><br/> <b>SR-Low_a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in C1N2 acc in SR-Low_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in C1N2 acc in SR-Low_a-EWK">C1N2</a>; <br/><br/> <b>SR-Low_b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-Low_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-Low_b-EWK">C1N2</a>; <br/><br/> <b>SR-Int_a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-Int_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-Int_a-EWK">C1N2</a>; <br/><br/> <b>SR-Int_b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-Int_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-Int_b-EWK">C1N2</a>; <br/><br/> <b>SR-High_16a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-High_16a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-High_16a-EWK">C1N2</a>; <br/><br/> <b>SR-High_16b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-High_16b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-High_16b-EWK">C1N2</a>; <br/><br/> <b>SR-High_8a-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-High_8a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-High_8a-EWK">C1N2</a>; <br/><br/> <b>SR-High_8b-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-High_8b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-High_8b-EWK">C1N2</a>; <br/><br/> <b>SR-1J-High-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-1J-High-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-1J-High-EWK">C1N2</a>; <br/><br/> <b>SR-llbb-EWK Signal Acceptance:</b><a href="116034?version=1&table=GMSB acc in SR-llbb-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 acc in SR-llbb-EWK">C1N2</a>; <br/><br/> <b>SR-OffShell_a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-OffShell_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-OffShell_a-EWK">C1N2</a>; <br/><br/> <b>SR-OffShell_b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-OffShell_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-OffShell_b-EWK">C1N2</a>; <br/><br/> <b>SR-Low_a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in C1N2 eff in SR-Low_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in C1N2 eff in SR-Low_a-EWK">C1N2</a>; <br/><br/> <b>SR-Low_b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-Low_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-Low_b-EWK">C1N2</a>; <br/><br/> <b>SR-Int_a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-Int_a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-Int_a-EWK">C1N2</a>; <br/><br/> <b>SR-Int_b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-Int_b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-Int_b-EWK">C1N2</a>; <br/><br/> <b>SR-High_16a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-High_16a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-High_16a-EWK">C1N2</a>; <br/><br/> <b>SR-High_16b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-High_16b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-High_16b-EWK">C1N2</a>; <br/><br/> <b>SR-High_8a-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-High_8a-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-High_8a-EWK">C1N2</a>; <br/><br/> <b>SR-High_8b-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-High_8b-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-High_8b-EWK">C1N2</a>; <br/><br/> <b>SR-1J-High-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-1J-High-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-1J-High-EWK">C1N2</a>; <br/><br/> <b>SR-llbb-EWK Signal Efficiency:</b><a href="116034?version=1&table=GMSB eff in SR-llbb-EWK">GMSB</a>; <a href="116034?version=1&table=C1N2 eff in SR-llbb-EWK">C1N2</a>; <br/><br/> <b>Truth Code snippets</b>, <b>SLHA files</b>, and <b>PYHF json likelihoods</b> are available under "Resources" (purple button on the left) ---- Record created with hepdata_lib 0.7.0: https://zenodo.org/record/4946277 and PYHF: https://doi.org/10.5281/zenodo.1169739
Breakdown of expected and observed yields in the two recursive-jigsaw reconstruction signal regions after a simultaneous fit of the the CRs. The two sets of regions are fit separately. The uncertainties include both statistical and systematic sources.
Breakdown of expected and observed yields in the electroweak search High and $\ell\ell bb$ signal regions after a simultaneous fit to the signal regions and control regions. All statistical and systematic uncertainties are included.
Breakdown of expected and observed yields in the electroweak search Int, Low, and OffShell signal regions after a simultaneous fit to the signal regions and control regions. All statistical and systematic uncertainties are included.
Breakdown of expected and observed yields in the four edge signal regions, integrated over the $m_{\ell\ell}$ distribution after a separate simultaneous fit to each signal region and control region pair. The uncertainties include both the statistical and systematic sources.
Breakdown of expected and observed yields in the three on-$Z$ signal regions after a separate simultaneous fit to each signal region and control region pair. The uncertainties include both the statistical and systematic sources.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.
Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.
Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.
Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.
Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.
Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.
Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.
Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.
Observed and expected dilepton mass distributions in VR3L-STR without a fit to the data. The 'Other' category includes the negligible contributions from $t\bar{t}$ and $Z/\gamma^*$+jets processes. The hatched band contains the statistical uncertainty and the theoretical systematic uncertainties of the $WZ$/$ZZ$ prediction, which are the dominant sources of uncertainty. No fit is performed. The last bin contains the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.
Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.
Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.
Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.
Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294]. The grey numbers indicate the observed 95\% CLs upper limit on the cross section.
Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294]. The grey numbers indicate the observed 95$\%$ CLs upper limit on the cross section.
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].
The combined $E_{\mathrm{T}}^{\mathrm{miss}}$ distribution of VRLow-STR and SRLow-STR (left), and the same region with the $\Delta\phi(\boldsymbol{j}_{1,2},\boldsymbol{\mathit{p}}_{ ext{T}}^{ ext{miss}})<0.4$ requirement, used as a control region to normalize the $Z/\gamma^*+\mathrm{jets}$ process (right). Separate fits for the SR and VR are performed, as for the results in the paper, and the resulting distributions are merged. All statistical and systematic uncertainties are included in the hatched bands. The last bins contain the overflow.
Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.
Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.
Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.
Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.
Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.
Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.
Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.
Table 36: Cutflow of expected events in the region SR-OffShell_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 36: Cutflow of expected events in the region SR-OffShell_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 37: Cutflow of expected events in the region SR-OffShell_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 37: Cutflow of expected events in the region SR-OffShell_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 38: Cutflow of expected events in the region SR-Low_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 38: Cutflow of expected events in the region SR-Low_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 39: Cutflow of expected events in the region SR-Low_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 39: Cutflow of expected events in the region SR-Low_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 40: Cutflow of expected events in the region SR-Low-2-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 40: Cutflow of expected events in the region SR-Low-2-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 41: Cutflow of expected events in the region SR-Int_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 41: Cutflow of expected events in the region SR-Int_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 42: Cutflow of expected events in the region SR-Int_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 42: Cutflow of expected events in the region SR-Int_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 43: Cutflow of expected events in the region SR-High_16a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 43: Cutflow of expected events in the region SR-High_16a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 44: Cutflow of expected events in the region SR-High_16b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 44: Cutflow of expected events in the region SR-High_16b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 45: Cutflow of expected events in the region SR-High_8a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 45: Cutflow of expected events in the region SR-High_8a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 46: Cutflow of expected events in the region SR-High_8b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 46: Cutflow of expected events in the region SR-High_8b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 47: Cutflow of expected events in the region SR-1J-High-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 47: Cutflow of expected events in the region SR-1J-High-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 48: Cutflow of expected events in the region SR-llbb-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
Table 48: Cutflow of expected events in the region SR-llbb-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.
The combined $m_{jj}$ distribution of CR-Z-EWK and SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in CR-Z-met-EWK (right), which removes the upper limit of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}}) < 9$ from the definition of CR-Z-EWK. This $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ tail overlaps other control and validation regions, but not signal regions. The arrows indicate the signal region SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ phase space which is not included in CR-Z-EWK (right). All EWK search control and signal regions are included in the fit. All statistical and systematic uncertainties are included in the hatched bands. The theoretical uncertainties from CR-Z-EWK are applied to CR-Z-met-EWK. The last bins contain the overflow.
The combined $m_{jj}$ distribution of CR-Z-EWK and SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in CR-Z-met-EWK (right), which removes the upper limit of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}}) < 9$ from the definition of CR-Z-EWK. This $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ tail overlaps other control and validation regions, but not signal regions. The arrows indicate the signal region SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ phase space which is not included in CR-Z-EWK (right). All EWK search control and signal regions are included in the fit. All statistical and systematic uncertainties are included in the hatched bands. The theoretical uncertainties from CR-Z-EWK are applied to CR-Z-met-EWK. The last bins contain the overflow.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.
Results are presented from a search for physics beyond the standard model in proton-proton collisions at $\sqrt{s} =$ 13 TeV in channels with two Higgs bosons, each decaying via the process H $\to$$\mathrm{b\bar{b}}$, and large missing transverse momentum. The search uses a data sample corresponding to an integrated luminosity of 137 fb$^{-1}$ collected by the CMS experiment at the CERN LHC. The search is motivated by models of supersymmetry that predict the production of neutralinos, the neutral partners of the electroweak gauge and Higgs bosons. The observed event yields in the signal regions are found to be consistent with the standard model background expectations. The results are interpreted using simplified models of supersymmetry. For the electroweak production of nearly mass-degenerate higgsinos, each of whose decay chains yields a neutralino ($\tilde{\chi}^0_1$) that in turn decays to a massless goldstino and a Higgs boson, $\tilde{\chi}^0_1$ masses in the range 175 to 1025 GeV are excluded at 95% confidence level. For the strong production of gluino pairs decaying via a slightly lighter $\tilde{\chi}^0_2$ to H and a light $\tilde{\chi}^0_1$, gluino masses below 2330 GeV are excluded.
Predicted background and observed yields vs bin index
Cross section 95% CL upper limit vs m($\widetilde{\chi}^0_1$) for SMS model TChiHH-G.
Theory cross sections vs m($\widetilde{\chi}^0_1$) for SMS model TChiHH-G.
Theory cross sections vs m($\widetilde{\chi}^0_1$) for SMS model TChiHH-G, $\widetilde{\chi}^0_1, \widetilde{\chi}^0_2$ NLSPs only.
The 95% CL observed and expected upper limits on the production cross sections of the TChiHH signal model as a function of the $\widetilde{\chi}^0_2$ and $\widetilde{\chi}^0_1$ masses. Relative to Fig. 13, the binning of the table is adjusted to align with the model points that were sampled. In addition, a small correction has been applied that affects the cross section upper limits for points with $m(\widetilde{\chi}^0_1) = 1$ GeV. For that row of points in the figure, the small region of model phase space reached by both the resolved and boosted selections was inadvertently double counted in computing the signal contribution to the predicted yield.
Exclusion limits assuming the approximate-NNLO+NNLL cross sections
Exclusion limits assuming the approximate-NNLO+NNLL cross sections
Exclusion limits assuming the approximate-NNLO+NNLL cross sections
Exclusion limits assuming the approximate-NNLO+NNLL cross sections
Exclusion limits assuming the approximate-NNLO+NNLL cross sections
Exclusion limits assuming the approximate-NNLO+NNLL cross sections
Cross section, 95% CL upper limit plus theory vs m($\widetilde{g}$) for SMS model T5HH.
Pre-fit background covariance matrix $\sigma_{xy}$ for the 22 analysis bins, ordered as in Fig. 10.
Pre-fit background correlation matrix $\rho_{xy}$ for the 22 analysis bins, ordered as in Fig. 10.
Significance vs $(m(\widetilde{\chi}^0_2), m(\widetilde{\chi}^0_1))$ for SMS model TChiHH.
Significance vs $m(\widetilde{g})$ for SMS model T5HH.
Likelihood profile at $(m(\widetilde{\chi}^0_2) = $275, $m(\widetilde{\chi}^0_1) = $1) for SMS model TChiHH.
Likelihood profile at $(m(\widetilde{\chi}^0_2) = $300, $m(\widetilde{\chi}^0_1) = $1) for SMS model TChiHH.
Likelihood profile at $(m(\widetilde{\chi}^0_2) = $300, $m(\widetilde{\chi}^0_1) = $50) for SMS model TChiHH.
Likelihood profile at $(m(\widetilde{\chi}^0_2) = $450, $m(\widetilde{\chi}^0_1) = $1) for SMS model TChiHH.
Likelihood profile at $(m(\widetilde{\chi}^0_2) = $750, $m(\widetilde{\chi}^0_1) = $1) for SMS model TChiHH.
Likelihood profile at $(m(\widetilde{\chi}^0_2) = $750, $m(\widetilde{\chi}^0_1) = $200) for SMS model TChiHH.
Efficiency vs $m(\widetilde{\chi}^0_1)$ for SMS model TChiHH-G. The denominator includes all 22 signal regions, and assumes $\mathcal{B}(\mathrm{H}$-->$\mathrm{b}\overline{\mathrm{b}})=100\%$.
Efficiency vs $m(\widetilde{g})$ for SMS model T5HH. The denominator includes all 22 signal regions, and no constraint on $\mathcal{B}(\mathrm{H}$-->$\mathrm{b}\overline{\mathrm{b}})$.
Efficiency vs $(m(\widetilde{\chi}^0_2), m(\widetilde{\chi}^0_1))$ for SMS model TChiHH. The denominator includes all 22 signal regions, and assumes $\mathcal{B}(\mathrm{H}$-->$\mathrm{b}\overline{\mathrm{b}})=100\%$.
Signal and control region populations for the resolved signature.
Signal and control region populations for the boosted signature.
Cut flow table for the resolved topology showing expected event yields at 137 fb$^{-1}$ for selected signal models as successive selections are applied.
Cut flow table for the boosted topology showing expected event yields at 137 fb$^{-1}$ for selected signal model points as successive selections are applied. Here the hadronic baseline is defined by $N_{\mathrm{jet}}\geq$2, \ptmiss$>$300 GeV, $\Delta\phi$ cuts, and the isolated lepton and track vetoes.
A search for supersymmetry in events with two or three low-momentum leptons and missing transverse momentum is performed. The search uses proton-proton collisions at $\sqrt{s} =$ 13 TeV collected in the three-year period 2016-2018 by the CMS experiment at the LHC and corresponding to an integrated luminosity of up to 137 fb$^{-1}$. The data are found to be in agreement with expectations from standard model processes. The results are interpreted in terms of electroweakino and top squark pair production with a small mass difference between the produced supersymmetric particles and the lightest neutralino. For the electroweakino interpretation, two simplified models are used, a wino-bino model and a higgsino model. Exclusion limits at 95% confidence level are set on $\widetilde{\chi}^0_2 / \widetilde{\chi}^\pm_1$ masses up to 275 GeV for a mass difference of 10 GeV in the wino-bino case, and up to 205 (150) GeV for a mass difference of 7.5 (3) GeV in the higgsino case. The results for the higgsino are further interpreted using a phenomenological minimal supersymmetric standard model, excluding the higgsino mass parameter $\mu$ up to 180 GeV with the bino mass parameter $M_1$ at 800 GeV. In the top squark interpretation, exclusion limits are set at top squark masses up to 540 GeV for four-body top squark decays and up to 480 GeV for chargino-mediated decays with a mass difference of 30 GeV.
A search for new phenomena in final states with hadronically decaying tau leptons, $b$-jets, and missing transverse momentum is presented. The analyzed dataset comprises $pp$~collision data at a center-of-mass energy of $\sqrt s = 13$ TeV with an integrated luminosity of 139/fb, delivered by the Large Hadron Collider and recorded with the ATLAS detector from 2015 to 2018. The observed data are compatible with the expected Standard Model background. The results are interpreted in simplified models for two different scenarios. The first model is based on supersymmetry and considers pair production of top squarks, each of which decays into a $b$-quark, a neutrino and a tau slepton. Each tau slepton in turn decays into a tau lepton and a nearly massless gravitino. Within this model, top-squark masses up to 1.4 TeV can be excluded at the 95% confidence level over a wide range of tau-slepton masses. The second model considers pair production of leptoquarks with decays into third-generation leptons and quarks. Depending on the branching fraction into charged leptons, leptoquarks with masses up to around 1.25 TeV can be excluded at the 95% confidence level for the case of scalar leptoquarks and up to 1.8 TeV (1.5 TeV) for vector leptoquarks in a Yang--Mills (minimal-coupling) scenario. In addition, model-independent upper limits are set on the cross section of processes beyond the Standard Model.
Relative systematic uncertainties in the estimated number of background events in the signal regions. In the lower part of the table, a breakdown of the total uncertainty into different categories is given. For the multi-bin SR, the breakdown refers to the integral over all three $p_{\text{T}}(\tau)$ bins. As the individual uncertainties are correlated, they do not add in quadrature to equal the total background uncertainty.
Distributions of $m_{\text{T}2}(\tau_{1},\tau_{2})$ in the di-tau SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.
Distributions of $E_{\text{T}}^{\text{miss}}$ in the di-tau SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.
Distributions of $s_{\text{T}}$ in the single-tau one-bin SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.
Distributions of $m_{\text{T}}(\tau)$ in the single-tau one-bin SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.
Distributions of $\Sigma m_{\text{T}}(b_{1,2})$ in the single-tau $p_{\text{T}}(\tau)$-binned SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.
Distributions of $p_{\text{T}}(\tau)$ in the single-tau $p_{\text{T}}(\tau)$-binned SR. The stacked histograms show the various SM background contributions. The hatched band indicates the total statistical and systematic uncertainty of the SM background. The $t\bar{t}$ (2 real $\tau$) and $t\bar{t}$ (1 real $\tau$) as well as the single-top background contributions are scaled with the normalization factors obtained from the background-only fit. Minor backgrounds are grouped together and denoted as 'Other'. This includes $t\bar{t}$-fake, single top, and other top (di-tau channel) or $t\bar{t}$-fake, $t\bar{t}+H$, multiboson, and other top (single-tau channel). The overlaid dotted lines show the additional contributions for signal scenarios close to the expected exclusion contour with the particle type and the mass and $\beta$ parameters for the simplified models indicated in the legend. For the leptoquark signal model the shapes of the distributions for $\text{LQ}_{3}^{\text{d}}$ and $\text{LQ}_{3}^{\text{v}}$ (not shown) are similar to that of $\text{LQ}_{3}^{\text{u}}$. The rightmost bin includes the overflow.
Observed event yields in data ('Observed') and expected event yields for SM background processes obtained from the background-only fit ('Total bkg.' and rows below) in the signal regions of the di-tau and single-tau channels. The quoted uncertainties include both the statistical and systematic uncertainties and are truncated at zero yield. By construction, no $t\bar{t}$ (2 real $\tau$) events can pass the selections in the single-tau channel. As the individual uncertainties are correlated, they do not add in quadrature to equal the total background uncertainty.
From left to right: upper limits at the 95% confidence level (CL) on the visible cross section ($\sigma_\text{vis}$) and on the number of signal events ($S_{\text{obs}}^{95}$). The third column ($S_{\text{exp}}^{95}$) shows the upper limit at the 95% CL on the number of signal events, given the expected number (and $\pm 1\,\sigma$ excursions on the expectation) of background events. The last two columns indicate the confidence level observed for the background-only hypothesis ($\text{CL}_{b}$), the discovery $p$-value ($p(s=0)$) and the significance ($Z$). In the di-tau SR, where fewer events are observed than predicted by the fitted background estimate, the $p$-value is capped at 0.5.
Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for the minimal-coupling scenario. The limits are derived from the binned single-tau signal region.
Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for the minimal-coupling scenario. The limits are derived from the binned single-tau signal region.
Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for vector leptoquarks with additional gauge couplings. The limits are derived from the binned single-tau signal region.
Expected and observed exclusion contours at the 95% confidence level for the vector third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{v}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ into a quark and a charged lepton. The plot shows the exclusion contour for vector leptoquarks with additional gauge couplings. The limits are derived from the binned single-tau signal region.
Exclusion contours at the 95% confidence level for the stop-stau signal model as a function of the masses of the top squark $m(\tilde{t}_{1})$ and of the tau slepton $m(\tilde{\tau}_{1})$. Expected and observed limits are shown for the present search in comparison to observed limits from previous ATLAS analyses based on data from Run-1 of the LHC at $\sqrt{s} = 8$ TeV [Eur. Phys. J. C 76 (2016)] and on a partial dataset from Run 2 at $\sqrt{s} = 13$ TeV [Phys. Rev. D 98 (2018) 032008]. The green band indicates the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments.
Exclusion contours at the 95% confidence level for the stop-stau signal model as a function of the masses of the top squark $m(\tilde{t}_{1})$ and of the tau slepton $m(\tilde{\tau}_{1})$. Expected and observed limits are shown for the present search in comparison to observed limits from previous ATLAS analyses based on data from Run-1 of the LHC at $\sqrt{s} = 8$ TeV [Eur. Phys. J. C 76 (2016)] and on a partial dataset from Run 2 at $\sqrt{s} = 13$ TeV [Phys. Rev. D 98 (2018) 032008]. The green band indicates the limit on the mass of the tau slepton (for a massless LSP) from the LEP experiments.
Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{u}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for up-type leptoquarks $\text{LQ}_{3}^{\text{u}})$ with charge $+2/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.
Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{u}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for up-type leptoquarks $\text{LQ}_{3}^{\text{u}})$ with charge $+2/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.
Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{d}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for down-type leptoquarks $\text{LQ}_{3}^{\text{d}})$ with charge $-1/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.
Expected and observed exclusion contours at the 95% confidence level for the scalar third-generation leptoquark signal model, as a function of the mass $m(\text{LQ}_{3}^{\text{d}})$ and the branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ into a quark and a charged lepton. The plot shows the exclusion contour for down-type leptoquarks $\text{LQ}_{3}^{\text{d}})$ with charge $-1/3e$. The limits are derived from the binned single-tau signal region. Shown in gray for comparison are the observed exclusion-limit contours from the previous ATLAS publication that targets the same leptoquark models but is based on a subset of the Run-2 data [JHEP 06 (2019) 144]. In this previous publication five different analyses are considered that target not only the final state studied here but also the final states that correspond to a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow q\ell)$ of 0 or 1, leading to the concave shapes of the gray exclusion contours.
Upper limits on the signal cross section at the 95 % confidence level for the stop-stau signal model.
Upper limits on the signal cross section at the 95 % confidence level for the scalar third-generation leptoquark signal model with up-type leptoquarks.
Upper limits on the signal cross section at the 95 % confidence level for the scalar third-generation leptoquark signal model with down-type leptoquarks.
Upper limits on the signal cross section at the 95 % confidence level for the vector third-generation leptoquark signal model with minimal coupling (MC).
Upper limits on the signal cross section at the 95 % confidence level for the vector third-generation leptoquark signal model with additional gauge couplings (YM).
Acceptance of the one-bin signal region of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.
Efficiency of the one-bin signal region of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.
Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.
Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.
Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the signal region of the di-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$.
Efficiency of the signal region of the di-tau channel for pair production of up-type leptoquarks $\text{LQ}_{3}^{\text{u}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{u}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the one-bin signal region of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.
Efficiency of the one-bin signal region of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.
Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.
Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.
Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the signal region of the di-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$.
Efficiency of the signal region of the di-tau channel for pair production of down-type leptoquarks $\text{LQ}_{3}^{\text{d}}$. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{d}} \rightarrow t\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.
Efficiency of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.
Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.
Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.
Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario.
Efficiency of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ in the minimal-coupling scenario. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.
Efficiency of the one-bin signal region of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.
Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.
Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.
Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 or 1 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings.
Efficiency of the signal region of the di-tau channel for pair production of vector leptoquarks $\text{LQ}_{3}^{\text{v}}$ with additional gauge couplings. The plot does not show efficiencies for a branching fraction $B(\text{LQ}_{3}^{\text{v}} \rightarrow b\tau)$ of 0 because here the acceptance at generator level becomes zero and the efficiency is thus undefined.
Acceptance of the one-bin signal region of the single-tau channel for pair production of top squarks with decays via tau sleptons.
Efficiency of the one-bin signal region of the single-tau channel for pair production of top squarks with decays via tau sleptons.
Acceptance of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.
Efficiency of the first bin of the multi-bin signal region (50 GeV $< p_{\text{T}}(\tau) <$ 100 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.
Acceptance of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.
Efficiency of the middle bin of the multi-bin signal region (100 GeV $< p_{\text{T}}(\tau) <$ 200 GeV) of the single-tau channel for pair production of top squarks with decays via tau sleptons.
Acceptance of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of top squarks with decays via tau sleptons.
Efficiency of the last bin of the multi-bin signal region (200 GeV $< p_{\text{T}}(\tau)$) of the single-tau channel for pair production of top squarks with decays via tau sleptons.
Acceptance of the signal region of the di-tau channel for pair production of top squarks with decays via tau sleptons.
Efficiency of the signal region of the di-tau channel for pair production of top squarks with decays via tau sleptons.
Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the di-tau SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.
Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the single-tau one-bin SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\tilde{t}_{1}) = 1350$ GeV, $m(\tilde{\tau}_{1}) = 1090$ GeV for the single-tau multi-bin SR. The simulated sample contains 30,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the di-tau SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau one-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{u}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau multi-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the di-tau SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau one-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{d}}) = 1.2$ TeV, $\beta = 0.5$ for the single-tau multi-bin SR. The simulated sample contains 210,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the di-tau SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the single-tau one-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the minimal-coupling scenario for the single-tau multi-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the di-tau SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the di-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the single-tau one-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
Cutflow for the benchmark signal model $m(\text{LQ}_{3}^{\text{v}}) = 1.4$ TeV, $\beta = 0.5$ in the Yang--Mills scenario for the single-tau multi-bin SR. The simulated sample contains 50,000 raw MC events. Weighted event yields are reported, normalized to an integrated luminosity of 139 fb$^{-1}$. 'Preselection' refers to the preselection for the single-tau channel.
A search for chargino$-$neutralino pair production in three-lepton final states with missing transverse momentum is presented. The study is based on a dataset of $\sqrt{s} = 13$ TeV $pp$ collisions recorded with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 139 fb$^{-1}$. No significant excess relative to the Standard Model predictions is found in data. The results are interpreted in simplified models of supersymmetry, and statistically combined with results from a previous ATLAS search for compressed spectra in two-lepton final states. Various scenarios for the production and decay of charginos ($\tilde\chi^\pm_1$) and neutralinos ($\tilde\chi^0_2$) are considered. For pure higgsino $\tilde\chi^\pm_1\tilde\chi^0_2$ pair-production scenarios, exclusion limits at 95% confidence level are set on $\tilde\chi^0_2$ masses up to 210 GeV. Limits are also set for pure wino $\tilde\chi^\pm_1\tilde\chi^0_2$ pair production, on $\tilde\chi^0_2$ masses up to 640 GeV for decays via on-shell $W$ and $Z$ bosons, up to 300 GeV for decays via off-shell $W$ and $Z$ bosons, and up to 190 GeV for decays via $W$ and Standard Model Higgs bosons.
This is the HEPData space for the ATLAS SUSY EWK three-lepton search. The full resolution figures can be found at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SUSY-2019-09/ The full statistical likelihoods have been provided for this analysis. They can be downloaded by clicking on the purple 'Resources' button above and selecting the 'Common Resources' category. <b>Region yields:</b> <ul display="inline-block"> <li><a href="?table=Tab%2012%20Onshell%20WZ%20Signal%20Region%20Yields%20Table">Tab 12 Onshell WZ Signal Region Yields Table</a> <li><a href="?table=Tab%2013%20Onshell%20Wh%20Signal%20Region%20Yields%20Table">Tab 13 Onshell Wh Signal Region Yields Table</a> <li><a href="?table=Tab%2014%20Offshell%20low-$E_{T}^{miss}$%20Signal%20Region%20Yields%20Table">Tab 14 Offshell low-$E_{T}^{miss}$ Signal Region Yields Table</a> <li><a href="?table=Tab%2015%20Offshell%20high-$E_{T}^{miss}$%20Signal%20Region%20Yields%20Table">Tab 15 Offshell high-$E_{T}^{miss}$ Signal Region Yields Table</a> <li><a href="?table=Tab%2020%20RJR%20Signal%20Region%20Yields%20Table">Tab 20 RJR Signal Region Yields Table</a> <li><a href="?table=Fig%204%20Onshell%20Control%20and%20Validation%20Region%20Yields">Fig 4 Onshell Control and Validation Region Yields</a> <li><a href="?table=Fig%208%20Offshell%20Control%20and%20Validation%20Region%20Yields">Fig 8 Offshell Control and Validation Region Yields</a> <li><a href="?table=Fig%2010%20Onshell%20WZ%20Signal%20Region%20Yields">Fig 10 Onshell WZ Signal Region Yields</a> <li><a href="?table=Fig%2011%20Onshell%20Wh%20Signal%20Region%20Yields">Fig 11 Onshell Wh Signal Region Yields</a> <li><a href="?table=Fig%2012%20Offshell%20Signal%20Region%20Yields">Fig 12 Offshell Signal Region Yields</a> <li><a href="?table=Fig%2018%20RJR%20Control%20and%20Validation%20Region%20Yields">Fig 18 RJR Control and Validation Region Yields</a> </ul> <b>Exclusion contours:</b> <ul display="inline-block"> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Obs">Fig 16a WZ Exclusion: Wino-bino(+), Obs</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Obs_Up">Fig 16a WZ Exclusion: Wino-bino(+), Obs_Up</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Obs_Down">Fig 16a WZ Exclusion: Wino-bino(+), Obs_Down</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Exp">Fig 16a WZ Exclusion: Wino-bino(+), Exp</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Exp_Up">Fig 16a WZ Exclusion: Wino-bino(+), Exp_Up</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Exp_Down">Fig 16a WZ Exclusion: Wino-bino(+), Exp_Down</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20compressed_Obs">Fig 16a WZ Exclusion: Wino-bino(+), compressed_Obs</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20compressed_Exp">Fig 16a WZ Exclusion: Wino-bino(+), compressed_Exp</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20offshell_Obs">Fig 16a WZ Exclusion: Wino-bino(+), offshell_Obs</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20offshell_Exp">Fig 16a WZ Exclusion: Wino-bino(+), offshell_Exp</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20onshell_Obs">Fig 16a WZ Exclusion: Wino-bino(+), onshell_Obs</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20onshell_Exp">Fig 16a WZ Exclusion: Wino-bino(+), onshell_Exp</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Obs">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Obs</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Obs_Up">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Obs_Up</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Obs_Down">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Obs_Down</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Exp">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Exp</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Exp_Up">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Exp_Up</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Exp_Down">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Exp_Down</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20compressed_Obs">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), compressed_Obs</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20compressed_Exp">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), compressed_Exp</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20offshell_Obs">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), offshell_Obs</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20offshell_Exp">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), offshell_Exp</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20onshell_Obs">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), onshell_Obs</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20onshell_Exp">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), onshell_Exp</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Obs">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Obs</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Obs_Up">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Obs_Up</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Obs_Down">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Obs_Down</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Exp">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Exp</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Exp_Up">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Exp_Up</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Exp_Down">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Exp_Down</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20compressed_Obs">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), compressed_Obs</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20compressed_Exp">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), compressed_Exp</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20offshell_Obs">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), offshell_Obs</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20offshell_Exp">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), offshell_Exp</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Obs">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Obs</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Obs_Up">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Obs_Up</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Obs_Down">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Obs_Down</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Exp">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Exp</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Exp_Up">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Exp_Up</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Exp_Down">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Exp_Down</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20compressed_Obs">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), compressed_Obs</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20compressed_Exp">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), compressed_Exp</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20offshell_Obs">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), offshell_Obs</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20offshell_Exp">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), offshell_Exp</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Obs">Fig 17 Wh Exclusion, Obs</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Obs_Up">Fig 17 Wh Exclusion, Obs_Up</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Obs_Down">Fig 17 Wh Exclusion, Obs_Down</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Exp">Fig 17 Wh Exclusion, Exp</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Exp_Up">Fig 17 Wh Exclusion, Exp_Up</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Exp_Down">Fig 17 Wh Exclusion, Exp_Down</a> </ul> <b>Upper limits:</b> <ul display="inline-block"> <li><a href="?table=AuxFig%208a%20WZ%20Excl.%20Upper%20Limit%20Obs.%20Wino-bino(%2b)%20($\Delta%20m$)">AuxFig 8a WZ Excl. Upper Limit Obs. Wino-bino(+) ($\Delta m$)</a> <li><a href="?table=AuxFig%208b%20WZ%20Excl.%20Upper%20Limit%20Exp.%20Wino-bino(%2b)%20($\Delta%20m$)">AuxFig 8b WZ Excl. Upper Limit Exp. Wino-bino(+) ($\Delta m$)</a> <li><a href="?table=AuxFig%208c%20WZ%20Excl.%20Upper%20Limit%20Obs.%20Wino-bino(%2b)%20($\Delta%20m$)">AuxFig 8c WZ Excl. Upper Limit Obs. Wino-bino(+) ($\Delta m$)</a> <li><a href="?table=AuxFig%208d%20WZ%20Excl.%20Upper%20Limit%20Exp.%20Wino-bino(%2b)%20($\Delta%20m$)">AuxFig 8d WZ Excl. Upper Limit Exp. Wino-bino(+) ($\Delta m$)</a> <li><a href="?table=AuxFig%208e%20WZ%20Excl.%20Upper%20Limit%20Obs.%20Wino-bino(-)%20($\Delta%20m$)">AuxFig 8e WZ Excl. Upper Limit Obs. Wino-bino(-) ($\Delta m$)</a> <li><a href="?table=AuxFig%208f%20WZ%20Excl.%20Upper%20Limit%20Exp.%20Wino-bino(-)%20($\Delta%20m$)">AuxFig 8f WZ Excl. Upper Limit Exp. Wino-bino(-) ($\Delta m$)</a> <li><a href="?table=AuxFig%208g%20WZ%20Excl.%20Upper%20Limit%20Obs.%20Higgsino%20($\Delta%20m$)">AuxFig 8g WZ Excl. Upper Limit Obs. Higgsino ($\Delta m$)</a> <li><a href="?table=AuxFig%208h%20WZ%20Excl.%20Upper%20Limit%20Exp.%20Higgsino%20($\Delta%20m$)">AuxFig 8h WZ Excl. Upper Limit Exp. Higgsino ($\Delta m$)</a> <li><a href="?table=AuxFig%209a%20Wh%20Excl.%20Upper%20Limit%20Obs.">AuxFig 9a Wh Excl. Upper Limit Obs.</a> <li><a href="?table=AuxFig%209b%20Wh%20Excl.%20Upper%20Limit%20Exp.">AuxFig 9b Wh Excl. Upper Limit Exp.</a> </ul> <b>Model-independent discovery fits:</b> <ul display="inline-block"> <li><a href="?table=Tab%2018%20Onshell%20Discovery%20Fit%20Table">Tab 18 Onshell Discovery Fit Table</a> <li><a href="?table=Tab%2019%20Offshell%20Discovery%20Fit%20Table">Tab 19 Offshell Discovery Fit Table</a> <li><a href="?table=Tab%2021%20RJR%20Discovery%20Fit%20Table">Tab 21 RJR Discovery Fit Table</a> </ul> <b>Kinematic distributions:</b> <ul display="inline-block"> <li><a href="?table=Fig%2013a%20SR$_{DFOS}^{Wh}$-1%20($\Delta%20R_{OS,%20near}$)">Fig 13a SR$_{DFOS}^{Wh}$-1 ($\Delta R_{OS, near}$)</a> <li><a href="?table=Fig%2013b%20SR$_{DFOS}^{Wh}$-2%20(3rd%20Lep.%20$p_{T}$)">Fig 13b SR$_{DFOS}^{Wh}$-2 (3rd Lep. $p_{T}$)</a> <li><a href="?table=Fig%2013c%20SR$_{0j}^{WZ}$%20($E_{T}^{miss}$)">Fig 13c SR$_{0j}^{WZ}$ ($E_{T}^{miss}$)</a> <li><a href="?table=Fig%2013d%20SR$_{0j}^{WZ}$%20($m_{T}$)">Fig 13d SR$_{0j}^{WZ}$ ($m_{T}$)</a> <li><a href="?table=Fig%2014a%20SR$^{offWZ}_{LowETmiss}$-0j%20($m_{T}^{minmll}$)">Fig 14a SR$^{offWZ}_{LowETmiss}$-0j ($m_{T}^{minmll}$)</a> <li><a href="?table=Fig%2014b%20SR$^{offWZ}_{LowETmiss}$-nj%20($m_{T}^{minmll}$)">Fig 14b SR$^{offWZ}_{LowETmiss}$-nj ($m_{T}^{minmll}$)</a> <li><a href="?table=Fig%2014c%20SR$^{offWZ}_{HighETmiss}$-0j%20($m_{T}^{minmll}$)">Fig 14c SR$^{offWZ}_{HighETmiss}$-0j ($m_{T}^{minmll}$)</a> <li><a href="?table=Fig%2014d%20SR$^{offWZ}_{HighETmiss}$-nj%20($p_T^l%20\div%20E_T^{miss}$)">Fig 14d SR$^{offWZ}_{HighETmiss}$-nj ($p_T^l \div E_T^{miss}$)</a> <li><a href="?table=Fig%2020a%20RJR%20SR3$\ell$-Low%20($p_{T}^{\ell%201}$)">Fig 20a RJR SR3$\ell$-Low ($p_{T}^{\ell 1}$)</a> <li><a href="?table=Fig%2020b%20RJR%20SR3$\ell$-Low%20($H_{3,1}^{PP}$)">Fig 20b RJR SR3$\ell$-Low ($H_{3,1}^{PP}$)</a> <li><a href="?table=Fig%2020c%20RJR%20SR3$\ell$-ISR%20($p_{T~ISR}^{CM}$)">Fig 20c RJR SR3$\ell$-ISR ($p_{T~ISR}^{CM}$)</a> <li><a href="?table=Fig%2020d%20RJR%20SR3$\ell$-ISR%20($R_{ISR}$)">Fig 20d RJR SR3$\ell$-ISR ($R_{ISR}$)</a> </ul> <b>Cutflows:</b> <ul display="inline-block"> <li><a href="?table=AuxTab%205%20Cutflow:%20Onshell%20WZ">AuxTab 5 Cutflow: Onshell WZ</a> <li><a href="?table=AuxTab%206%20Cutflow:%20Onshell%20Wh">AuxTab 6 Cutflow: Onshell Wh</a> <li><a href="?table=AuxTab%207%20Cutflow:%20Offshell%20Wino-bino(%2b)%20(250,235)">AuxTab 7 Cutflow: Offshell Wino-bino(+) (250,235)</a> <li><a href="?table=AuxTab%208%20Cutflow:%20Offshell%20Wino-bino(%2b)%20(125,85)">AuxTab 8 Cutflow: Offshell Wino-bino(+) (125,85)</a> <li><a href="?table=AuxTab%209%20Cutflow:%20Offshell%20Wino-bino(%2b)%20(250,170)">AuxTab 9 Cutflow: Offshell Wino-bino(+) (250,170)</a> <li><a href="?table=AuxTab%2010%20Cutflow:%20Offshell%20Wino-bino(-)%20(250,235)">AuxTab 10 Cutflow: Offshell Wino-bino(-) (250,235)</a> <li><a href="?table=AuxTab%2011%20Cutflow:%20Offshell%20Wino-bino(-)%20(125,85)">AuxTab 11 Cutflow: Offshell Wino-bino(-) (125,85)</a> <li><a href="?table=AuxTab%2012%20Cutflow:%20Offshell%20Wino-bino(-)%20(250,170)">AuxTab 12 Cutflow: Offshell Wino-bino(-) (250,170)</a> <li><a href="?table=AuxTab%2013%20Cutflow:%20Offshell%20Higgsino%20(120,100)">AuxTab 13 Cutflow: Offshell Higgsino (120,100)</a> <li><a href="?table=AuxTab%2014%20Cutflow:%20Offshell%20Higgsino%20(100,40)">AuxTab 14 Cutflow: Offshell Higgsino (100,40)</a> <li><a href="?table=AuxTab%2015%20Cutflow:%20Offshell%20Higgsino%20(185,125)">AuxTab 15 Cutflow: Offshell Higgsino (185,125)</a> </ul> <b>Acceptances and Efficiencies:</b> <ul display="inline-block"> <li><a href="?table=AuxFig%2010a%20Acc:%20Onshell%20SR$_{0j}^{WZ}$">AuxFig 10a Acc: Onshell SR$_{0j}^{WZ}$</a> <li><a href="?table=AuxFig%2010b%20Eff:%20Onshell%20SR$_{0j}^{WZ}$">AuxFig 10b Eff: Onshell SR$_{0j}^{WZ}$</a> <li><a href="?table=AuxFig%2010c%20Acc:%20Onshell%20SR$_{nj}^{WZ}$">AuxFig 10c Acc: Onshell SR$_{nj}^{WZ}$</a> <li><a href="?table=AuxFig%2010d%20Eff:%20Onshell%20SR$_{nj}^{WZ}$">AuxFig 10d Eff: Onshell SR$_{nj}^{WZ}$</a> <li><a href="?table=AuxFig%2011a%20Acc:%20Onshell%20SR$_{low-m_{ll}-0j}^{Wh}$">AuxFig 11a Acc: Onshell SR$_{low-m_{ll}-0j}^{Wh}$</a> <li><a href="?table=AuxFig%2011b%20Eff:%20Onshell%20SR$_{low-m_{ll}-0j}^{Wh}$">AuxFig 11b Eff: Onshell SR$_{low-m_{ll}-0j}^{Wh}$</a> <li><a href="?table=AuxFig%2011c%20Acc:%20Onshell%20SR$_{low-m_{ll}-nj}^{Wh}$">AuxFig 11c Acc: Onshell SR$_{low-m_{ll}-nj}^{Wh}$</a> <li><a href="?table=AuxFig%2011d%20Eff:%20Onshell%20SR$_{low-m_{ll}-nj}^{Wh}$">AuxFig 11d Eff: Onshell SR$_{low-m_{ll}-nj}^{Wh}$</a> <li><a href="?table=AuxFig%2011e%20Acc:%20Onshell%20SR$_{DFOS}^{Wh}$">AuxFig 11e Acc: Onshell SR$_{DFOS}^{Wh}$</a> <li><a href="?table=AuxFig%2011f%20Eff:%20Onshell%20SR$_{DFOS}^{Wh}$">AuxFig 11f Eff: Onshell SR$_{DFOS}^{Wh}$</a> <li><a href="?table=AuxFig%2012a%20Acc:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 12a Acc: Off. Wino-bino(+) SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2012b%20Eff:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 12b Eff: Off. Wino-bino(+) SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2012c%20Acc:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 12c Acc: Off. Wino-bino(+) SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2012d%20Eff:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 12d Eff: Off. Wino-bino(+) SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2012e%20Acc:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 12e Acc: Off. Wino-bino(+) SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2012f%20Eff:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 12f Eff: Off. Wino-bino(+) SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2012g%20Acc:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 12g Acc: Off. Wino-bino(+) SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2012h%20Eff:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 12h Eff: Off. Wino-bino(+) SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2013a%20Acc:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 13a Acc: Off. Wino-bino(-) SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2013b%20Eff:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 13b Eff: Off. Wino-bino(-) SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2013c%20Acc:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 13c Acc: Off. Wino-bino(-) SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2013d%20Eff:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 13d Eff: Off. Wino-bino(-) SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2013e%20Acc:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 13e Acc: Off. Wino-bino(-) SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2013f%20Eff:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 13f Eff: Off. Wino-bino(-) SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2013g%20Acc:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 13g Acc: Off. Wino-bino(-) SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2013h%20Eff:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 13h Eff: Off. Wino-bino(-) SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2014a%20Acc:%20Off.%20Higgsino%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 14a Acc: Off. Higgsino SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2014b%20Eff:%20Off.%20Higgsino%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 14b Eff: Off. Higgsino SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2014c%20Acc:%20Off.%20Higgsino%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 14c Acc: Off. Higgsino SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2014d%20Eff:%20Off.%20Higgsino%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 14d Eff: Off. Higgsino SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2014e%20Acc:%20Off.%20Higgsino%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 14e Acc: Off. Higgsino SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2014f%20Eff:%20Off.%20Higgsino%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 14f Eff: Off. Higgsino SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2014g%20Acc:%20Off.%20Higgsino%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 14g Acc: Off. Higgsino SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2014h%20Eff:%20Off.%20Higgsino%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 14h Eff: Off. Higgsino SR$^{offWZ}_{highETmiss}$-nj</a> </ul>
This is the HEPData space for the ATLAS SUSY EWK three-lepton search. The full resolution figures can be found at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SUSY-2019-09/ The full statistical likelihoods have been provided for this analysis. They can be downloaded by clicking on the purple 'Resources' button above and selecting the 'Common Resources' category. <b>Region yields:</b> <ul display="inline-block"> <li><a href="?table=Tab%2012%20Onshell%20WZ%20Signal%20Region%20Yields%20Table">Tab 12 Onshell WZ Signal Region Yields Table</a> <li><a href="?table=Tab%2013%20Onshell%20Wh%20Signal%20Region%20Yields%20Table">Tab 13 Onshell Wh Signal Region Yields Table</a> <li><a href="?table=Tab%2014%20Offshell%20low-$E_{T}^{miss}$%20Signal%20Region%20Yields%20Table">Tab 14 Offshell low-$E_{T}^{miss}$ Signal Region Yields Table</a> <li><a href="?table=Tab%2015%20Offshell%20high-$E_{T}^{miss}$%20Signal%20Region%20Yields%20Table">Tab 15 Offshell high-$E_{T}^{miss}$ Signal Region Yields Table</a> <li><a href="?table=Tab%2020%20RJR%20Signal%20Region%20Yields%20Table">Tab 20 RJR Signal Region Yields Table</a> <li><a href="?table=Fig%204%20Onshell%20Control%20and%20Validation%20Region%20Yields">Fig 4 Onshell Control and Validation Region Yields</a> <li><a href="?table=Fig%208%20Offshell%20Control%20and%20Validation%20Region%20Yields">Fig 8 Offshell Control and Validation Region Yields</a> <li><a href="?table=Fig%2010%20Onshell%20WZ%20Signal%20Region%20Yields">Fig 10 Onshell WZ Signal Region Yields</a> <li><a href="?table=Fig%2011%20Onshell%20Wh%20Signal%20Region%20Yields">Fig 11 Onshell Wh Signal Region Yields</a> <li><a href="?table=Fig%2012%20Offshell%20Signal%20Region%20Yields">Fig 12 Offshell Signal Region Yields</a> <li><a href="?table=Fig%2018%20RJR%20Control%20and%20Validation%20Region%20Yields">Fig 18 RJR Control and Validation Region Yields</a> </ul> <b>Exclusion contours:</b> <ul display="inline-block"> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Obs">Fig 16a WZ Exclusion: Wino-bino(+), Obs</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Obs_Up">Fig 16a WZ Exclusion: Wino-bino(+), Obs_Up</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Obs_Down">Fig 16a WZ Exclusion: Wino-bino(+), Obs_Down</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Exp">Fig 16a WZ Exclusion: Wino-bino(+), Exp</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Exp_Up">Fig 16a WZ Exclusion: Wino-bino(+), Exp_Up</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20Exp_Down">Fig 16a WZ Exclusion: Wino-bino(+), Exp_Down</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20compressed_Obs">Fig 16a WZ Exclusion: Wino-bino(+), compressed_Obs</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20compressed_Exp">Fig 16a WZ Exclusion: Wino-bino(+), compressed_Exp</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20offshell_Obs">Fig 16a WZ Exclusion: Wino-bino(+), offshell_Obs</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20offshell_Exp">Fig 16a WZ Exclusion: Wino-bino(+), offshell_Exp</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20onshell_Obs">Fig 16a WZ Exclusion: Wino-bino(+), onshell_Obs</a> <li><a href="?table=Fig%2016a%20WZ%20Exclusion:%20Wino-bino(%2b),%20onshell_Exp">Fig 16a WZ Exclusion: Wino-bino(+), onshell_Exp</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Obs">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Obs</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Obs_Up">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Obs_Up</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Obs_Down">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Obs_Down</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Exp">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Exp</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Exp_Up">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Exp_Up</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20Exp_Down">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), Exp_Down</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20compressed_Obs">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), compressed_Obs</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20compressed_Exp">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), compressed_Exp</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20offshell_Obs">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), offshell_Obs</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20offshell_Exp">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), offshell_Exp</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20onshell_Obs">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), onshell_Obs</a> <li><a href="?table=Fig%2016b%20WZ%20Exclusion:%20Wino-bino(%2b)%20($\Delta%20m$),%20onshell_Exp">Fig 16b WZ Exclusion: Wino-bino(+) ($\Delta m$), onshell_Exp</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Obs">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Obs</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Obs_Up">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Obs_Up</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Obs_Down">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Obs_Down</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Exp">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Exp</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Exp_Up">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Exp_Up</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20Exp_Down">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), Exp_Down</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20compressed_Obs">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), compressed_Obs</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20compressed_Exp">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), compressed_Exp</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20offshell_Obs">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), offshell_Obs</a> <li><a href="?table=Fig%2016c%20WZ%20Exclusion:%20Wino-bino(-)%20($\Delta%20m$),%20offshell_Exp">Fig 16c WZ Exclusion: Wino-bino(-) ($\Delta m$), offshell_Exp</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Obs">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Obs</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Obs_Up">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Obs_Up</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Obs_Down">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Obs_Down</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Exp">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Exp</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Exp_Up">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Exp_Up</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20Exp_Down">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), Exp_Down</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20compressed_Obs">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), compressed_Obs</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20compressed_Exp">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), compressed_Exp</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20offshell_Obs">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), offshell_Obs</a> <li><a href="?table=Fig%2016d%20WZ%20Exclusion:%20Higgsino%20($\Delta%20m$),%20offshell_Exp">Fig 16d WZ Exclusion: Higgsino ($\Delta m$), offshell_Exp</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Obs">Fig 17 Wh Exclusion, Obs</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Obs_Up">Fig 17 Wh Exclusion, Obs_Up</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Obs_Down">Fig 17 Wh Exclusion, Obs_Down</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Exp">Fig 17 Wh Exclusion, Exp</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Exp_Up">Fig 17 Wh Exclusion, Exp_Up</a> <li><a href="?table=Fig%2017%20Wh%20Exclusion,%20Exp_Down">Fig 17 Wh Exclusion, Exp_Down</a> </ul> <b>Upper limits:</b> <ul display="inline-block"> <li><a href="?table=AuxFig%208a%20WZ%20Excl.%20Upper%20Limit%20Obs.%20Wino-bino(%2b)%20($\Delta%20m$)">AuxFig 8a WZ Excl. Upper Limit Obs. Wino-bino(+) ($\Delta m$)</a> <li><a href="?table=AuxFig%208b%20WZ%20Excl.%20Upper%20Limit%20Exp.%20Wino-bino(%2b)%20($\Delta%20m$)">AuxFig 8b WZ Excl. Upper Limit Exp. Wino-bino(+) ($\Delta m$)</a> <li><a href="?table=AuxFig%208c%20WZ%20Excl.%20Upper%20Limit%20Obs.%20Wino-bino(%2b)%20($\Delta%20m$)">AuxFig 8c WZ Excl. Upper Limit Obs. Wino-bino(+) ($\Delta m$)</a> <li><a href="?table=AuxFig%208d%20WZ%20Excl.%20Upper%20Limit%20Exp.%20Wino-bino(%2b)%20($\Delta%20m$)">AuxFig 8d WZ Excl. Upper Limit Exp. Wino-bino(+) ($\Delta m$)</a> <li><a href="?table=AuxFig%208e%20WZ%20Excl.%20Upper%20Limit%20Obs.%20Wino-bino(-)%20($\Delta%20m$)">AuxFig 8e WZ Excl. Upper Limit Obs. Wino-bino(-) ($\Delta m$)</a> <li><a href="?table=AuxFig%208f%20WZ%20Excl.%20Upper%20Limit%20Exp.%20Wino-bino(-)%20($\Delta%20m$)">AuxFig 8f WZ Excl. Upper Limit Exp. Wino-bino(-) ($\Delta m$)</a> <li><a href="?table=AuxFig%208g%20WZ%20Excl.%20Upper%20Limit%20Obs.%20Higgsino%20($\Delta%20m$)">AuxFig 8g WZ Excl. Upper Limit Obs. Higgsino ($\Delta m$)</a> <li><a href="?table=AuxFig%208h%20WZ%20Excl.%20Upper%20Limit%20Exp.%20Higgsino%20($\Delta%20m$)">AuxFig 8h WZ Excl. Upper Limit Exp. Higgsino ($\Delta m$)</a> <li><a href="?table=AuxFig%209a%20Wh%20Excl.%20Upper%20Limit%20Obs.">AuxFig 9a Wh Excl. Upper Limit Obs.</a> <li><a href="?table=AuxFig%209b%20Wh%20Excl.%20Upper%20Limit%20Exp.">AuxFig 9b Wh Excl. Upper Limit Exp.</a> </ul> <b>Model-independent discovery fits:</b> <ul display="inline-block"> <li><a href="?table=Tab%2018%20Onshell%20Discovery%20Fit%20Table">Tab 18 Onshell Discovery Fit Table</a> <li><a href="?table=Tab%2019%20Offshell%20Discovery%20Fit%20Table">Tab 19 Offshell Discovery Fit Table</a> <li><a href="?table=Tab%2021%20RJR%20Discovery%20Fit%20Table">Tab 21 RJR Discovery Fit Table</a> </ul> <b>Kinematic distributions:</b> <ul display="inline-block"> <li><a href="?table=Fig%2013a%20SR$_{DFOS}^{Wh}$-1%20($\Delta%20R_{OS,%20near}$)">Fig 13a SR$_{DFOS}^{Wh}$-1 ($\Delta R_{OS, near}$)</a> <li><a href="?table=Fig%2013b%20SR$_{DFOS}^{Wh}$-2%20(3rd%20Lep.%20$p_{T}$)">Fig 13b SR$_{DFOS}^{Wh}$-2 (3rd Lep. $p_{T}$)</a> <li><a href="?table=Fig%2013c%20SR$_{0j}^{WZ}$%20($E_{T}^{miss}$)">Fig 13c SR$_{0j}^{WZ}$ ($E_{T}^{miss}$)</a> <li><a href="?table=Fig%2013d%20SR$_{0j}^{WZ}$%20($m_{T}$)">Fig 13d SR$_{0j}^{WZ}$ ($m_{T}$)</a> <li><a href="?table=Fig%2014a%20SR$^{offWZ}_{LowETmiss}$-0j%20($m_{T}^{minmll}$)">Fig 14a SR$^{offWZ}_{LowETmiss}$-0j ($m_{T}^{minmll}$)</a> <li><a href="?table=Fig%2014b%20SR$^{offWZ}_{LowETmiss}$-nj%20($m_{T}^{minmll}$)">Fig 14b SR$^{offWZ}_{LowETmiss}$-nj ($m_{T}^{minmll}$)</a> <li><a href="?table=Fig%2014c%20SR$^{offWZ}_{HighETmiss}$-0j%20($m_{T}^{minmll}$)">Fig 14c SR$^{offWZ}_{HighETmiss}$-0j ($m_{T}^{minmll}$)</a> <li><a href="?table=Fig%2014d%20SR$^{offWZ}_{HighETmiss}$-nj%20($p_T^l%20\div%20E_T^{miss}$)">Fig 14d SR$^{offWZ}_{HighETmiss}$-nj ($p_T^l \div E_T^{miss}$)</a> <li><a href="?table=Fig%2020a%20RJR%20SR3$\ell$-Low%20($p_{T}^{\ell%201}$)">Fig 20a RJR SR3$\ell$-Low ($p_{T}^{\ell 1}$)</a> <li><a href="?table=Fig%2020b%20RJR%20SR3$\ell$-Low%20($H_{3,1}^{PP}$)">Fig 20b RJR SR3$\ell$-Low ($H_{3,1}^{PP}$)</a> <li><a href="?table=Fig%2020c%20RJR%20SR3$\ell$-ISR%20($p_{T~ISR}^{CM}$)">Fig 20c RJR SR3$\ell$-ISR ($p_{T~ISR}^{CM}$)</a> <li><a href="?table=Fig%2020d%20RJR%20SR3$\ell$-ISR%20($R_{ISR}$)">Fig 20d RJR SR3$\ell$-ISR ($R_{ISR}$)</a> </ul> <b>Cutflows:</b> <ul display="inline-block"> <li><a href="?table=AuxTab%205%20Cutflow:%20Onshell%20WZ">AuxTab 5 Cutflow: Onshell WZ</a> <li><a href="?table=AuxTab%206%20Cutflow:%20Onshell%20Wh">AuxTab 6 Cutflow: Onshell Wh</a> <li><a href="?table=AuxTab%207%20Cutflow:%20Offshell%20Wino-bino(%2b)%20(250,235)">AuxTab 7 Cutflow: Offshell Wino-bino(+) (250,235)</a> <li><a href="?table=AuxTab%208%20Cutflow:%20Offshell%20Wino-bino(%2b)%20(125,85)">AuxTab 8 Cutflow: Offshell Wino-bino(+) (125,85)</a> <li><a href="?table=AuxTab%209%20Cutflow:%20Offshell%20Wino-bino(%2b)%20(250,170)">AuxTab 9 Cutflow: Offshell Wino-bino(+) (250,170)</a> <li><a href="?table=AuxTab%2010%20Cutflow:%20Offshell%20Wino-bino(-)%20(250,235)">AuxTab 10 Cutflow: Offshell Wino-bino(-) (250,235)</a> <li><a href="?table=AuxTab%2011%20Cutflow:%20Offshell%20Wino-bino(-)%20(125,85)">AuxTab 11 Cutflow: Offshell Wino-bino(-) (125,85)</a> <li><a href="?table=AuxTab%2012%20Cutflow:%20Offshell%20Wino-bino(-)%20(250,170)">AuxTab 12 Cutflow: Offshell Wino-bino(-) (250,170)</a> <li><a href="?table=AuxTab%2013%20Cutflow:%20Offshell%20Higgsino%20(120,100)">AuxTab 13 Cutflow: Offshell Higgsino (120,100)</a> <li><a href="?table=AuxTab%2014%20Cutflow:%20Offshell%20Higgsino%20(100,40)">AuxTab 14 Cutflow: Offshell Higgsino (100,40)</a> <li><a href="?table=AuxTab%2015%20Cutflow:%20Offshell%20Higgsino%20(185,125)">AuxTab 15 Cutflow: Offshell Higgsino (185,125)</a> </ul> <b>Acceptances and Efficiencies:</b> <ul display="inline-block"> <li><a href="?table=AuxFig%2010a%20Acc:%20Onshell%20SR$_{0j}^{WZ}$">AuxFig 10a Acc: Onshell SR$_{0j}^{WZ}$</a> <li><a href="?table=AuxFig%2010b%20Eff:%20Onshell%20SR$_{0j}^{WZ}$">AuxFig 10b Eff: Onshell SR$_{0j}^{WZ}$</a> <li><a href="?table=AuxFig%2010c%20Acc:%20Onshell%20SR$_{nj}^{WZ}$">AuxFig 10c Acc: Onshell SR$_{nj}^{WZ}$</a> <li><a href="?table=AuxFig%2010d%20Eff:%20Onshell%20SR$_{nj}^{WZ}$">AuxFig 10d Eff: Onshell SR$_{nj}^{WZ}$</a> <li><a href="?table=AuxFig%2011a%20Acc:%20Onshell%20SR$_{low-m_{ll}-0j}^{Wh}$">AuxFig 11a Acc: Onshell SR$_{low-m_{ll}-0j}^{Wh}$</a> <li><a href="?table=AuxFig%2011b%20Eff:%20Onshell%20SR$_{low-m_{ll}-0j}^{Wh}$">AuxFig 11b Eff: Onshell SR$_{low-m_{ll}-0j}^{Wh}$</a> <li><a href="?table=AuxFig%2011c%20Acc:%20Onshell%20SR$_{low-m_{ll}-nj}^{Wh}$">AuxFig 11c Acc: Onshell SR$_{low-m_{ll}-nj}^{Wh}$</a> <li><a href="?table=AuxFig%2011d%20Eff:%20Onshell%20SR$_{low-m_{ll}-nj}^{Wh}$">AuxFig 11d Eff: Onshell SR$_{low-m_{ll}-nj}^{Wh}$</a> <li><a href="?table=AuxFig%2011e%20Acc:%20Onshell%20SR$_{DFOS}^{Wh}$">AuxFig 11e Acc: Onshell SR$_{DFOS}^{Wh}$</a> <li><a href="?table=AuxFig%2011f%20Eff:%20Onshell%20SR$_{DFOS}^{Wh}$">AuxFig 11f Eff: Onshell SR$_{DFOS}^{Wh}$</a> <li><a href="?table=AuxFig%2012a%20Acc:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 12a Acc: Off. Wino-bino(+) SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2012b%20Eff:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 12b Eff: Off. Wino-bino(+) SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2012c%20Acc:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 12c Acc: Off. Wino-bino(+) SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2012d%20Eff:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 12d Eff: Off. Wino-bino(+) SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2012e%20Acc:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 12e Acc: Off. Wino-bino(+) SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2012f%20Eff:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 12f Eff: Off. Wino-bino(+) SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2012g%20Acc:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 12g Acc: Off. Wino-bino(+) SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2012h%20Eff:%20Off.%20Wino-bino(%2b)%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 12h Eff: Off. Wino-bino(+) SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2013a%20Acc:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 13a Acc: Off. Wino-bino(-) SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2013b%20Eff:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 13b Eff: Off. Wino-bino(-) SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2013c%20Acc:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 13c Acc: Off. Wino-bino(-) SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2013d%20Eff:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 13d Eff: Off. Wino-bino(-) SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2013e%20Acc:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 13e Acc: Off. Wino-bino(-) SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2013f%20Eff:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 13f Eff: Off. Wino-bino(-) SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2013g%20Acc:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 13g Acc: Off. Wino-bino(-) SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2013h%20Eff:%20Off.%20Wino-bino(-)%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 13h Eff: Off. Wino-bino(-) SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2014a%20Acc:%20Off.%20Higgsino%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 14a Acc: Off. Higgsino SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2014b%20Eff:%20Off.%20Higgsino%20SR$^{offWZ}_{lowETmiss}$-0j">AuxFig 14b Eff: Off. Higgsino SR$^{offWZ}_{lowETmiss}$-0j</a> <li><a href="?table=AuxFig%2014c%20Acc:%20Off.%20Higgsino%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 14c Acc: Off. Higgsino SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2014d%20Eff:%20Off.%20Higgsino%20SR$^{offWZ}_{lowETmiss}$-nj">AuxFig 14d Eff: Off. Higgsino SR$^{offWZ}_{lowETmiss}$-nj</a> <li><a href="?table=AuxFig%2014e%20Acc:%20Off.%20Higgsino%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 14e Acc: Off. Higgsino SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2014f%20Eff:%20Off.%20Higgsino%20SR$^{offWZ}_{highETmiss}$-0j">AuxFig 14f Eff: Off. Higgsino SR$^{offWZ}_{highETmiss}$-0j</a> <li><a href="?table=AuxFig%2014g%20Acc:%20Off.%20Higgsino%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 14g Acc: Off. Higgsino SR$^{offWZ}_{highETmiss}$-nj</a> <li><a href="?table=AuxFig%2014h%20Eff:%20Off.%20Higgsino%20SR$^{offWZ}_{highETmiss}$-nj">AuxFig 14h Eff: Off. Higgsino SR$^{offWZ}_{highETmiss}$-nj</a> </ul>
Comparison of the observed data and expected SM background yields in the CRs (pre-fit) and VRs (post-fit) of the onshell $W\!Z$ and $W\!h$ selections. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. The bottom panel shows the relative difference between the observed data and expected yields for the CRs and the significance of the difference for the VRs, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Comparison of the observed data and expected SM background yields in the CRs (pre-fit) and VRs (post-fit) of the onshell $W\!Z$ and $W\!h$ selections. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. The bottom panel shows the relative difference between the observed data and expected yields for the CRs and the significance of the difference for the VRs, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Comparison of the observed data and expected SM background yields in the CRs and VRs of the offshell $W\!Z$ selection. The SM prediction is taken from the background-only fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Comparison of the observed data and expected SM background yields in the CRs and VRs of the offshell $W\!Z$ selection. The SM prediction is taken from the background-only fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Observed and expected yields after the background-only fit in the SRs for the onshell $W\!Z$ selection. The normalization factors of the $W\!Z$ sample are extracted separately for the 0j, low-H<sub>T</sub> and high-H<sub>T</sub> regions, and are treated separately in the combined fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. Combined statistical and systematic uncertainties are presented.
Observed and expected yields after the background-only fit in the SRs for the onshell $W\!Z$ selection. The normalization factors of the $W\!Z$ sample are extracted separately for the 0j, low-H<sub>T</sub> and high-H<sub>T</sub> regions, and are treated separately in the combined fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. Combined statistical and systematic uncertainties are presented.
Observed and expected yields after the background-only fit in the SRs for the $W\!h$ selection. The normalization factors of the $W\!Z$ sample are extracted separately for the 0j, low-H<sub>T</sub> and high-H<sub>T</sub> regions, and are treated separately in the combined fit. The "Others" category contains the single-top, WW, tt̄+X and rare top processes. Combined statistical and systematic uncertainties are presented.
Observed and expected yields after the background-only fit in the SRs for the $W\!h$ selection. The normalization factors of the $W\!Z$ sample are extracted separately for the 0j, low-H<sub>T</sub> and high-H<sub>T</sub> regions, and are treated separately in the combined fit. The "Others" category contains the single-top, WW, tt̄+X and rare top processes. Combined statistical and systematic uncertainties are presented.
Comparison of the observed data and expected SM background yields in the SRs of the onshell $W\!Z$ selection. The SM prediction is taken from the background-only fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Comparison of the observed data and expected SM background yields in the SRs of the onshell $W\!Z$ selection. The SM prediction is taken from the background-only fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Comparison of the observed data and expected SM background yields in the SRs of the $W\!h$ selection. The SM prediction is taken from the background-only fit. The "Others" category contains the single-top, WW, tt̄+X and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Comparison of the observed data and expected SM background yields in the SRs of the $W\!h$ selection. The SM prediction is taken from the background-only fit. The "Others" category contains the single-top, WW, tt̄+X and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Observed and expected yields after the background-only fit in SR<sup>offWZ</sup><sub>lowETmiss</sub>. The normalization factors of the $W\!Z$ sample extracted separately for 0j and nj, and are treated separately in the combined fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. Combined statistical and systematic uncertainties are presented.
Observed and expected yields after the background-only fit in SR<sup>offWZ</sup><sub>lowETmiss</sub>. The normalization factors of the $W\!Z$ sample extracted separately for 0j and nj, and are treated separately in the combined fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. Combined statistical and systematic uncertainties are presented.
Observed and expected yields after the background-only fit in SR<sup>offWZ</sup><sub>highETmiss</sub>. The normalization factors of the $W\!Z$ sample extracted separately for 0j and nj, and are treated separately in the combined fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. Combined statistical and systematic uncertainties are presented.
Observed and expected yields after the background-only fit in SR<sup>offWZ</sup><sub>highETmiss</sub>. The normalization factors of the $W\!Z$ sample extracted separately for 0j and nj, and are treated separately in the combined fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. Combined statistical and systematic uncertainties are presented.
Comparison of the observed data and expected SM background yields in the SRs of the offshell $W\!Z$ selection. The SM prediction is taken from the background-only fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W^{*}\!Z^{*}$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Comparison of the observed data and expected SM background yields in the SRs of the offshell $W\!Z$ selection. The SM prediction is taken from the background-only fit. The "Others" category contains the single-top, WW, triboson, Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W^{*}\!Z^{*}$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the onshell $W\!Z$ and $W\!h$ selections. The figure shows (a) the ΔR<sub>OS,near</sub> distribution in SR<sup>Wh</sup><sub>DF</sub>-1, (b) the 3rd leading lepton p<sub>T</sub> in SR<sup>Wh</sup><sub>DF</sub>-2, and the (c) E<sub>T</sub><sup>miss</sup> and (d) m<sub>T</sub> distributions in SR<sup>WZ</sup><sub>0j</sub> (with all SR-i bins of SR<sup>WZ</sup><sub>0j</sub> summed up). The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes, except in the top panels, where triboson and Higgs production contributions are shown separately, and tt̄+X is merged into Others. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$/$W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the onshell $W\!Z$ and $W\!h$ selections. The figure shows (a) the ΔR<sub>OS,near</sub> distribution in SR<sup>Wh</sup><sub>DF</sub>-1, (b) the 3rd leading lepton p<sub>T</sub> in SR<sup>Wh</sup><sub>DF</sub>-2, and the (c) E<sub>T</sub><sup>miss</sup> and (d) m<sub>T</sub> distributions in SR<sup>WZ</sup><sub>0j</sub> (with all SR-i bins of SR<sup>WZ</sup><sub>0j</sub> summed up). The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes, except in the top panels, where triboson and Higgs production contributions are shown separately, and tt̄+X is merged into Others. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$/$W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the onshell $W\!Z$ and $W\!h$ selections. The figure shows (a) the ΔR<sub>OS,near</sub> distribution in SR<sup>Wh</sup><sub>DF</sub>-1, (b) the 3rd leading lepton p<sub>T</sub> in SR<sup>Wh</sup><sub>DF</sub>-2, and the (c) E<sub>T</sub><sup>miss</sup> and (d) m<sub>T</sub> distributions in SR<sup>WZ</sup><sub>0j</sub> (with all SR-i bins of SR<sup>WZ</sup><sub>0j</sub> summed up). The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes, except in the top panels, where triboson and Higgs production contributions are shown separately, and tt̄+X is merged into Others. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$/$W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the onshell $W\!Z$ and $W\!h$ selections. The figure shows (a) the ΔR<sub>OS,near</sub> distribution in SR<sup>Wh</sup><sub>DF</sub>-1, (b) the 3rd leading lepton p<sub>T</sub> in SR<sup>Wh</sup><sub>DF</sub>-2, and the (c) E<sub>T</sub><sup>miss</sup> and (d) m<sub>T</sub> distributions in SR<sup>WZ</sup><sub>0j</sub> (with all SR-i bins of SR<sup>WZ</sup><sub>0j</sub> summed up). The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes, except in the top panels, where triboson and Higgs production contributions are shown separately, and tt̄+X is merged into Others. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$/$W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the onshell $W\!Z$ and $W\!h$ selections. The figure shows (a) the ΔR<sub>OS,near</sub> distribution in SR<sup>Wh</sup><sub>DF</sub>-1, (b) the 3rd leading lepton p<sub>T</sub> in SR<sup>Wh</sup><sub>DF</sub>-2, and the (c) E<sub>T</sub><sup>miss</sup> and (d) m<sub>T</sub> distributions in SR<sup>WZ</sup><sub>0j</sub> (with all SR-i bins of SR<sup>WZ</sup><sub>0j</sub> summed up). The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes, except in the top panels, where triboson and Higgs production contributions are shown separately, and tt̄+X is merged into Others. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$/$W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the onshell $W\!Z$ and $W\!h$ selections. The figure shows (a) the ΔR<sub>OS,near</sub> distribution in SR<sup>Wh</sup><sub>DF</sub>-1, (b) the 3rd leading lepton p<sub>T</sub> in SR<sup>Wh</sup><sub>DF</sub>-2, and the (c) E<sub>T</sub><sup>miss</sup> and (d) m<sub>T</sub> distributions in SR<sup>WZ</sup><sub>0j</sub> (with all SR-i bins of SR<sup>WZ</sup><sub>0j</sub> summed up). The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes, except in the top panels, where triboson and Higgs production contributions are shown separately, and tt̄+X is merged into Others. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$/$W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the onshell $W\!Z$ and $W\!h$ selections. The figure shows (a) the ΔR<sub>OS,near</sub> distribution in SR<sup>Wh</sup><sub>DF</sub>-1, (b) the 3rd leading lepton p<sub>T</sub> in SR<sup>Wh</sup><sub>DF</sub>-2, and the (c) E<sub>T</sub><sup>miss</sup> and (d) m<sub>T</sub> distributions in SR<sup>WZ</sup><sub>0j</sub> (with all SR-i bins of SR<sup>WZ</sup><sub>0j</sub> summed up). The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes, except in the top panels, where triboson and Higgs production contributions are shown separately, and tt̄+X is merged into Others. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$/$W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the onshell $W\!Z$ and $W\!h$ selections. The figure shows (a) the ΔR<sub>OS,near</sub> distribution in SR<sup>Wh</sup><sub>DF</sub>-1, (b) the 3rd leading lepton p<sub>T</sub> in SR<sup>Wh</sup><sub>DF</sub>-2, and the (c) E<sub>T</sub><sup>miss</sup> and (d) m<sub>T</sub> distributions in SR<sup>WZ</sup><sub>0j</sub> (with all SR-i bins of SR<sup>WZ</sup><sub>0j</sub> summed up). The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes, except in the top panels, where triboson and Higgs production contributions are shown separately, and tt̄+X is merged into Others. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$/$W\!h$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the offshell $W\!Z$ selection. The figure shows the m<sub>T</sub><sup>m<sub>ll</sub>min</sup> distribution in (a) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj and (c) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and the |p<sub>T</sub><sup>lep</sup>|/E<sub>T</sub><sup>miss</sup> distribution in (d) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj. The contributing m<sub>ll</sub><sup>min</sup> mass bins within each SR<sup>offWZ</sup> category are summed together. The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the offshell $W\!Z$ selection. The figure shows the m<sub>T</sub><sup>m<sub>ll</sub>min</sup> distribution in (a) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj and (c) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and the |p<sub>T</sub><sup>lep</sup>|/E<sub>T</sub><sup>miss</sup> distribution in (d) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj. The contributing m<sub>ll</sub><sup>min</sup> mass bins within each SR<sup>offWZ</sup> category are summed together. The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the offshell $W\!Z$ selection. The figure shows the m<sub>T</sub><sup>m<sub>ll</sub>min</sup> distribution in (a) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj and (c) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and the |p<sub>T</sub><sup>lep</sup>|/E<sub>T</sub><sup>miss</sup> distribution in (d) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj. The contributing m<sub>ll</sub><sup>min</sup> mass bins within each SR<sup>offWZ</sup> category are summed together. The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the offshell $W\!Z$ selection. The figure shows the m<sub>T</sub><sup>m<sub>ll</sub>min</sup> distribution in (a) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj and (c) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and the |p<sub>T</sub><sup>lep</sup>|/E<sub>T</sub><sup>miss</sup> distribution in (d) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj. The contributing m<sub>ll</sub><sup>min</sup> mass bins within each SR<sup>offWZ</sup> category are summed together. The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the offshell $W\!Z$ selection. The figure shows the m<sub>T</sub><sup>m<sub>ll</sub>min</sup> distribution in (a) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj and (c) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and the |p<sub>T</sub><sup>lep</sup>|/E<sub>T</sub><sup>miss</sup> distribution in (d) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj. The contributing m<sub>ll</sub><sup>min</sup> mass bins within each SR<sup>offWZ</sup> category are summed together. The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the offshell $W\!Z$ selection. The figure shows the m<sub>T</sub><sup>m<sub>ll</sub>min</sup> distribution in (a) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj and (c) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and the |p<sub>T</sub><sup>lep</sup>|/E<sub>T</sub><sup>miss</sup> distribution in (d) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj. The contributing m<sub>ll</sub><sup>min</sup> mass bins within each SR<sup>offWZ</sup> category are summed together. The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the offshell $W\!Z$ selection. The figure shows the m<sub>T</sub><sup>m<sub>ll</sub>min</sup> distribution in (a) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj and (c) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and the |p<sub>T</sub><sup>lep</sup>|/E<sub>T</sub><sup>miss</sup> distribution in (d) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj. The contributing m<sub>ll</sub><sup>min</sup> mass bins within each SR<sup>offWZ</sup> category are summed together. The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Kinematic distributions after the background-only fit showing the data and the post-fit expected background, in SRs of the offshell $W\!Z$ selection. The figure shows the m<sub>T</sub><sup>m<sub>ll</sub>min</sup> distribution in (a) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj and (c) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and the |p<sub>T</sub><sup>lep</sup>|/E<sub>T</sub><sup>miss</sup> distribution in (d) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj. The contributing m<sub>ll</sub><sup>min</sup> mass bins within each SR<sup>offWZ</sup> category are summed together. The SR selections are applied for each distribution, except for the variable shown, for which the selection is indicated by an arrow. The last bin includes overflow. The "Others" category contains backgrounds from single-top, WW, triboson, Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Observed (N<sub>obs</sub>) yields after the discovery-fit and expected (N<sub>exp</sub>) after the background-only fit, for the inclusive SRs of the onshell $W\!Z$ and $W\!h$ selections. The third and fourth column list the 95 CL upper limits on the visible cross-section (σ<sub>vis</sub><sup>95</sup>) and on the number of signal events (S<sub>obs</sub><sup>95</sup>). The fifth column (S<sub>exp</sub><sup>95</sup>) shows the 95 CL upper limit on the number of signal events, given the expected number (and ± 1σ excursions on the expectation) of background events. The last two columns indicate the CLb value, i.e. the confidence level observed for the background-only hypothesis, and the discovery p-value (p(s = 0)). If the observed yield is below the expected yield, the p-value is capped at 0.5.
Observed (N<sub>obs</sub>) yields after the discovery-fit and expected (N<sub>exp</sub>) after the background-only fit, for the inclusive SRs of the onshell $W\!Z$ and $W\!h$ selections. The third and fourth column list the 95 CL upper limits on the visible cross-section (σ<sub>vis</sub><sup>95</sup>) and on the number of signal events (S<sub>obs</sub><sup>95</sup>). The fifth column (S<sub>exp</sub><sup>95</sup>) shows the 95 CL upper limit on the number of signal events, given the expected number (and ± 1σ excursions on the expectation) of background events. The last two columns indicate the CLb value, i.e. the confidence level observed for the background-only hypothesis, and the discovery p-value (p(s = 0)). If the observed yield is below the expected yield, the p-value is capped at 0.5.
Observed (N<sub>obs</sub>) yields after the discovery-fit and expected (N<sub>exp</sub>) after the background-only fit, for the inclusive SRs of the offshell $W\!Z$ selection. The third and fourth column list the 95 CL upper limits on the visible cross section (σ<sub>vis</sub><sup>95</sup>) and on the number of signal events (S<sub>obs</sub><sup>95</sup>). The fifth column (S<sub>exp</sub><sup>95</sup>) shows the 95 CL upper limit on the number of signal events, given the expected number (and ± 1σ excursions on the expectation) of background events. The last two columns indicate the CLb value, i.e. the confidence level observed for the background-only hypothesis, and the discovery p-value (p(s = 0)). If the observed yield is below the expected yield, the p-value is capped at 0.5.
Observed (N<sub>obs</sub>) yields after the discovery-fit and expected (N<sub>exp</sub>) after the background-only fit, for the inclusive SRs of the offshell $W\!Z$ selection. The third and fourth column list the 95 CL upper limits on the visible cross section (σ<sub>vis</sub><sup>95</sup>) and on the number of signal events (S<sub>obs</sub><sup>95</sup>). The fifth column (S<sub>exp</sub><sup>95</sup>) shows the 95 CL upper limit on the number of signal events, given the expected number (and ± 1σ excursions on the expectation) of background events. The last two columns indicate the CLb value, i.e. the confidence level observed for the background-only hypothesis, and the discovery p-value (p(s = 0)). If the observed yield is below the expected yield, the p-value is capped at 0.5.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!Z$-mediated models in the (a,b) wino/bino (+) scenario, (c) the wino/bino (-) scenario, and (d) the higgsino scenario. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>exp</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties. The statistical combination of the onshell $W\!Z$, offshell $W\!Z$, and compressed results is shown as the main contour, while the observed (expected) limits for each individual selection are overlaid in green, blue, and orange solid (dashed) lines, respectively. The exclusion is shown projected (a) onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane or (b,c,d) onto the m(χ̃<sub>2</sub><sup>0</sup>) vs Δm plane. The light grey area denotes (top) the constraints obtained by the previous equivalent analysis in ATLAS using the 8 TeV 20.3 fb<sup>-1</sup> dataset [17], and (d) the LEP lower χ̃<sub>1</sub><sup>±</sup> mass limit [56]. The pale blue line in the top right panel represents the mass splitting range that yields a dark matter relic density equal to the observed relic density, Ω h<sup>2</sup>=0.1186±0.0020 [172], when the mass parameters of all the decoupled SUSY partners are set to 5 TeV and tanβ is chosen such that the SM-like Higgs boson mass is consistent with the observed value [43]. The area above (below) the blue line represents a dark-matter relic density larger (smaller) than the observed.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Exclusion limits obtained for the $W\!h$med in the wino/bino (+) scenario, calculated using the $W\!h$ SRs and projected onto the m(χ̃<sub>1</sub><sup>±</sup>, χ̃<sub>2</sub><sup>0</sup>) vs m(χ̃<sub>1</sub><sup>0</sup>) plane. The expected 95 CL sensitivity (dashed black line) is shown with ±1σ<sub>{exp}</sub> (yellow band) from experimental systematic uncertainties and statistical uncertainties on the data yields, the observed limit (red solid line) is shown with ±1σ<sub>theory</sub> (dotted red lines) from signal cross-section uncertainties.
Comparison of the observed data and expected SM background yields in the CRs and VRs of the RJR selection. The SM prediction is taken from the background-only fit. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Comparison of the observed data and expected SM background yields in the CRs and VRs of the RJR selection. The SM prediction is taken from the background-only fit. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. The hatched band indicates the combined theoretical, experimental, and MC statistical uncertainties. The bottom panel shows the significance of the difference between the observed and expected yields, calculated with the profile likelihood method from [169], adding a minus sign if the yield is below the prediction.
Observed and expected yields after the background-only fit in the SRs for the RJR selection. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Combined statistical and systematic uncertainties are presented.
Observed and expected yields after the background-only fit in the SRs for the RJR selection. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Combined statistical and systematic uncertainties are presented.
Example of kinematic distributions after the background-only fit, showing the data and the post-fit expected background, in regions of the RJR selection. The figure shows the (a) p<sub>T</sub><sup>ℓ<sub>1</sub></sup> and (b) H<sup>PP</sup><sub>3,1</sub> distributions in SR3ℓ-Low, and the (c) p<sup>CM</sup><sub>T ISR</sub> and (d) R<sub>ISR</sub> distributions in SR3ℓ-ISR. The last bin includes overflow. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Example of kinematic distributions after the background-only fit, showing the data and the post-fit expected background, in regions of the RJR selection. The figure shows the (a) p<sub>T</sub><sup>ℓ<sub>1</sub></sup> and (b) H<sup>PP</sup><sub>3,1</sub> distributions in SR3ℓ-Low, and the (c) p<sup>CM</sup><sub>T ISR</sub> and (d) R<sub>ISR</sub> distributions in SR3ℓ-ISR. The last bin includes overflow. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Example of kinematic distributions after the background-only fit, showing the data and the post-fit expected background, in regions of the RJR selection. The figure shows the (a) p<sub>T</sub><sup>ℓ<sub>1</sub></sup> and (b) H<sup>PP</sup><sub>3,1</sub> distributions in SR3ℓ-Low, and the (c) p<sup>CM</sup><sub>T ISR</sub> and (d) R<sub>ISR</sub> distributions in SR3ℓ-ISR. The last bin includes overflow. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Example of kinematic distributions after the background-only fit, showing the data and the post-fit expected background, in regions of the RJR selection. The figure shows the (a) p<sub>T</sub><sup>ℓ<sub>1</sub></sup> and (b) H<sup>PP</sup><sub>3,1</sub> distributions in SR3ℓ-Low, and the (c) p<sup>CM</sup><sub>T ISR</sub> and (d) R<sub>ISR</sub> distributions in SR3ℓ-ISR. The last bin includes overflow. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Example of kinematic distributions after the background-only fit, showing the data and the post-fit expected background, in regions of the RJR selection. The figure shows the (a) p<sub>T</sub><sup>ℓ<sub>1</sub></sup> and (b) H<sup>PP</sup><sub>3,1</sub> distributions in SR3ℓ-Low, and the (c) p<sup>CM</sup><sub>T ISR</sub> and (d) R<sub>ISR</sub> distributions in SR3ℓ-ISR. The last bin includes overflow. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Example of kinematic distributions after the background-only fit, showing the data and the post-fit expected background, in regions of the RJR selection. The figure shows the (a) p<sub>T</sub><sup>ℓ<sub>1</sub></sup> and (b) H<sup>PP</sup><sub>3,1</sub> distributions in SR3ℓ-Low, and the (c) p<sup>CM</sup><sub>T ISR</sub> and (d) R<sub>ISR</sub> distributions in SR3ℓ-ISR. The last bin includes overflow. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Example of kinematic distributions after the background-only fit, showing the data and the post-fit expected background, in regions of the RJR selection. The figure shows the (a) p<sub>T</sub><sup>ℓ<sub>1</sub></sup> and (b) H<sup>PP</sup><sub>3,1</sub> distributions in SR3ℓ-Low, and the (c) p<sup>CM</sup><sub>T ISR</sub> and (d) R<sub>ISR</sub> distributions in SR3ℓ-ISR. The last bin includes overflow. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Example of kinematic distributions after the background-only fit, showing the data and the post-fit expected background, in regions of the RJR selection. The figure shows the (a) p<sub>T</sub><sup>ℓ<sub>1</sub></sup> and (b) H<sup>PP</sup><sub>3,1</sub> distributions in SR3ℓ-Low, and the (c) p<sup>CM</sup><sub>T ISR</sub> and (d) R<sub>ISR</sub> distributions in SR3ℓ-ISR. The last bin includes overflow. The "FNP leptons" category contains backgrounds from tt̄, tW, WW and Z+jets processes. The "Others" category contains backgrounds from Higgs and rare top processes. Distributions for wino/bino (+) χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> → $W\!Z$ signals are overlaid, with mass values given as (m(χ̃<sub>1</sub><sup>±</sup>),m(χ̃<sub>1</sub><sup>0</sup>)) GeV. The bottom panel shows the ratio of the observed data to the predicted yields. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
{Results of the discovery-fit for the SRs of the RJR selection, calculated using pseudo-experiments.} The first and second column list the 95 CL upper limits on the visible cross section (σ<sub>vis</sub><sup>95</sup>) and on the number of signal events (S<sub>obs</sub><sup>95</sup>). The third column (S<sub>exp</sub><sup>95</sup>) shows the 95 CL upper limit on the number of signal events, given the expected number (and ± 1σ excursions on the expectation) of background events. The last two columns indicate the CLb value, i.e. the confidence level observed for the background-only hypothesis, and the discovery p-value (p(s = 0)). If the observed yield is below the expected yield, the p-value is capped at 0.5. vspace{0.5em}
{Results of the discovery-fit for the SRs of the RJR selection, calculated using pseudo-experiments.} The first and second column list the 95 CL upper limits on the visible cross section (σ<sub>vis</sub><sup>95</sup>) and on the number of signal events (S<sub>obs</sub><sup>95</sup>). The third column (S<sub>exp</sub><sup>95</sup>) shows the 95 CL upper limit on the number of signal events, given the expected number (and ± 1σ excursions on the expectation) of background events. The last two columns indicate the CLb value, i.e. the confidence level observed for the background-only hypothesis, and the discovery p-value (p(s = 0)). If the observed yield is below the expected yield, the p-value is capped at 0.5. vspace{0.5em}
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!Z$-mediated model, for the (1st and 2nd row) wino/bino (+) scenario, (3rd row) the wino/bino (-) scenario, and (4th row) the higgsino scenario, as in Figure 16. Black numbers represent the observed (a) and expected (b) upper cross-section limits.
Exclusion limits obtained for the $W\!h$-mediated model, for the wino/bino (+) scenario, as in Figure 17. The black numbers represent the observed (a,c,e,g) and expected (b,d,f,h) upper cross-section limits.
Exclusion limits obtained for the $W\!h$-mediated model, for the wino/bino (+) scenario, as in Figure 17. The black numbers represent the observed (a,c,e,g) and expected (b,d,f,h) upper cross-section limits.
Exclusion limits obtained for the $W\!h$-mediated model, for the wino/bino (+) scenario, as in Figure 17. The black numbers represent the observed (a,c,e,g) and expected (b,d,f,h) upper cross-section limits.
Exclusion limits obtained for the $W\!h$-mediated model, for the wino/bino (+) scenario, as in Figure 17. The black numbers represent the observed (a,c,e,g) and expected (b,d,f,h) upper cross-section limits.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c) truth-level acceptances and (b,d) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>WZ</sup><sub>0j</sub>, (c,d) SR<sup>WZ</sup><sub>nj</sub> regions of the onshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c) truth-level acceptances and (b,d) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>WZ</sup><sub>0j</sub>, (c,d) SR<sup>WZ</sup><sub>nj</sub> regions of the onshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c) truth-level acceptances and (b,d) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>WZ</sup><sub>0j</sub>, (c,d) SR<sup>WZ</sup><sub>nj</sub> regions of the onshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c) truth-level acceptances and (b,d) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>WZ</sup><sub>0j</sub>, (c,d) SR<sup>WZ</sup><sub>nj</sub> regions of the onshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c) truth-level acceptances and (b,d) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>WZ</sup><sub>0j</sub>, (c,d) SR<sup>WZ</sup><sub>nj</sub> regions of the onshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c) truth-level acceptances and (b,d) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>WZ</sup><sub>0j</sub>, (c,d) SR<sup>WZ</sup><sub>nj</sub> regions of the onshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c) truth-level acceptances and (b,d) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>WZ</sup><sub>0j</sub>, (c,d) SR<sup>WZ</sup><sub>nj</sub> regions of the onshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c) truth-level acceptances and (b,d) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>WZ</sup><sub>0j</sub>, (c,d) SR<sup>WZ</sup><sub>nj</sub> regions of the onshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e) truth-level acceptances and (b,d,f) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-0j</sub>, (c,d) SR<sup>Wh</sup><sub>low-m<sub>ll</sub>-nj</sub>, and (e,f) SR<sup>Wh</sup><sub>DF</sub> regions of the $W\!h$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (+) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the wino/bino (-) scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
The χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> (a,c,e,g) truth-level acceptances and (b,d,f,h) reconstruction efficiencies for the higgsino scenario, in the inclusive (a,b) SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, (c,d) SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, (e,f) SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and (g,h) SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions of the offshell $W\!Z$ selection, after MC-to-data efficiency weights are applied.
Summary of onshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (300,200) GeV and m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (600,100) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal points, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks per inclusive regions, and then further for each SR. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5.
Summary of onshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (300,200) GeV and m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (600,100) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal points, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks per inclusive regions, and then further for each SR. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5.
Summary of $W\!h$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (190,60) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks per inclusive regions, and then further for each SR. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5.
Summary of $W\!h$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (190,60) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks per inclusive regions, and then further for each SR. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (250,235) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (250,235) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (125,85) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (125,85) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (250,170) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (250,170) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (+) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (250,235) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (-) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (250,235) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (-) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (125,85) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (-) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (125,85) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (-) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (250,170) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (-) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (250,170) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the wino/bino (-) interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (120,100) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the higgsino interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (120,100) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the higgsino interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (100,40) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the higgsino interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (100,40) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the higgsino interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (185,125) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the higgsino interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
Summary of offshell $W\!Z$ event selections for the m(χ̃<sub>2</sub><sup>0</sup>,χ̃<sub>1</sub><sup>0</sup>) = (185,125) GeV χ̃<sub>1</sub><sup>±</sup>/χ̃<sub>2</sub><sup>0</sup> signal point, for the higgsino interpretation. The yields are normalised to a luminosity of 139 fb<sup>-1</sup>, and MC-to-data efficiency weights from triggering and from the reconstruction and identification of individual physics objects are applied to the final yields in each signal region. After the initial selections, the table is split in row blocks for the inclusive SR<sup>offWZ</sup><sub>lowETmiss</sub>-0j, SR<sup>offWZ</sup><sub>lowETmiss</sub>-nj, SR<sup>offWZ</sup><sub>highETmiss</sub>-0j, and SR<sup>offWZ</sup><sub>highETmiss</sub>-nj regions, with the individual SR results in columns. The inclusive OR of regions a through g2 is given in the last column. Selection details per bin are indicated in bracketed blue as relevant, and the final yield for each SR is highlighted in bold green at the end of each block. The generator filters are discussed in detail in Section 4. The "3 isolated lepton selection" includes the common event selection as discussed in Section 5 and the initial SFOS lepton pair selection.
A search for supersymmetry in events with four or more charged leptons (electrons, muons and $\tau$-leptons) is presented. The analysis uses a data sample corresponding to $139\,\mbox{fb\(^{-1}\)}$ of proton-proton collisions delivered by the Large Hadron Collider at $\sqrt{s}=13$ TeV and recorded by the ATLAS detector. Four-lepton signal regions with up to two hadronically decaying $\tau$-leptons are designed to target several supersymmetric models, while a general five-lepton signal region targets any new physics phenomena leading to a final state with five charged leptons. Data yields are consistent with Standard Model expectations and results are used to set upper limits on contributions from processes beyond the Standard Model. Exclusion limits are set at the 95% confidence level in simplified models of general gauge-mediated supersymmetry, excluding higgsino masses up to $540$ GeV. In $R$-parity-violating simplified models with decays of the lightest supersymmetric particle to charged leptons, lower limits of $1.6$ TeV, $1.2$ TeV, and $2.5$ TeV are placed on wino, slepton and gluino masses, respectively.
The $E_{\mathrm{T}}^{\mathrm{miss}}$ distribution in SR0-ZZ$^{\mathrm{loose}}$ and SR0-ZZ$^{\mathrm{tight}}$ for events passing the signal region requirements except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $E_{\mathrm{T}}^{\mathrm{miss}}$ selections in the signal regions.
The $E_{\mathrm{T}}^{\mathrm{miss}}$ distribution in SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $E_{\mathrm{T}}^{\mathrm{miss}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $E_{\mathrm{T}}^{\mathrm{miss}}$ selections in the signal regions.
The $E_{\mathrm{T}}^{\mathrm{miss}}$ distribution in SR5L. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The $m_{\mathrm{eff}}$ distribution in SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.
The $m_{\mathrm{eff}}$ distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.
The $m_{\mathrm{eff}}$ distribution in SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.
The $m_{\mathrm{eff}}$ distribution in SR0$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.
The $m_{\mathrm{eff}}$ distribution in SR1$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.
The $m_{\mathrm{eff}}$ distribution in SR2$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.
Expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ bserved 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$+1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
$-1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.
Observed upper limit on the signal cross section in fb for the wino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Observed upper limit on the signal cross section in fb for the wino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Observed upper limit on the signal cross section in fb for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Observed upper limit on the signal cross section in fb for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Observed upper limit on the signal cross section in fb for the gluino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Observed upper limit on the signal cross section in fb for the gluino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Observed upper limit on the signal cross section in fb for the higgsino GGM models. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Best expected SR for the wino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.
Best expected SR for the wino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.
Best expected SR for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.
Best expected SR for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.
Best expected SR for the gluino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.
Best expected SR for the gluino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.
Best expected SR for the higgsino GGM models. A value of 6 corresponds to SR0-ZZ$^{\mathrm{loose}}$, 7 corresponds to SR0-ZZ$^{\mathrm{tight}}$, 8 corresponds to SR0-ZZ$^{\mathrm{loose}}_{\mathrm{bveto}}$, and 9 corresponds to SR0-ZZ$^{\mathrm{tight}}_{\mathrm{bveto}}$.
Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Acceptance across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
Efficiency across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.
The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$^{\mathrm{tight}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The $p_{\mathrm{T}}$ of the light leptons in distribution in SR5L. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The $p_{\mathrm{T}}$ of the light leptons in distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The $p_{\mathrm{T}}$ of the taus leptons in distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The $p_{\mathrm{T}}$ of the light taus in distribution in SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The lepton flavour and multiplicities in events with four light leptons and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The lepton flavour and multiplicities in events with four light leptons and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The lepton flavour and multiplicities in events with four light leptons and two Z candidates. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The lepton flavour and multiplicities in events with exactly five light leptons. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The lepton flavour and multiplicities in events with three light leptons and one tau and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The lepton flavour and multiplicities in events with three light leptons and one tau and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The lepton flavour and multiplicities in events with two light leptons and two taus and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
The lepton flavour and multiplicities in events with two light leptons and two taus and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.
Cutflow event yields in regions SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$, SR0$_{\mathrm{breq}}$, and SR5L for RPV models with the $\lambda_{12k}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.
Cutflow event yields in regions SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR1$_{\mathrm{breq}}$ for RPV models with the $\lambda_{i33}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.
Cutflow event yields in regions SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR2$_{\mathrm{breq}}$ for RPV models with the $\lambda_{i33}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.
Cutflow event yields in regions SR0-ZZ$^{\mathrm{loose}}$, SR0-ZZ$^{\mathrm{tight}}$, SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR5L the higgsino GGM RPC model with BR($\tilde{\chi}^{0}_1 \rightarrow Z \tilde{G}$) = 50% and higgsino masses of 200 GeV, or BR($\tilde{\chi}^{0}_1 \rightarrow Z \tilde{G}$) = 100% and higgsino masses of 300 GeV. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The generator filter is a selection of $\geq$4e/$\mu$/$\tau_{\mathrm{had-vis}}$ leptons with $p_{\mathrm{T}}(e,\mu)>4$GeV, $p_{\mathrm{T}}(\tau_{\mathrm{had-vis}})>15$GeV and $|\eta|<2.8$ and is applied during the MC generation of the simulated events. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} > 9$ GeV are required.
A search for pair production of bottom squarks in events with hadronically decaying $\tau$-leptons, $b$-tagged jets and large missing transverse momentum is presented. The analyzed dataset is based on proton-proton collisions at $\sqrt{s}$ = 13 TeV delivered by the Large Hadron Collider and recorded by the ATLAS detector from 2015 to 2018, and corresponds to an integrated luminosity of 139 fb$^{-1}$. The observed data are compatible with the expected Standard Model background. Results are interpreted in a simplified model where each bottom squark is assumed to decay into the second-lightest neutralino $\tilde \chi_2^0$ and a bottom quark, with $\tilde \chi_2^0$ decaying into a Higgs boson and the lightest neutralino $\tilde \chi_1^0$. The search focuses on final states where at least one Higgs boson decays into a pair of hadronically decaying $\tau$-leptons. This allows the acceptance and thus the sensitivity to be significantly improved relative to the previous results at low masses of the $\tilde \chi_2^0$, where bottom-squark masses up to 850 GeV are excluded at the 95% confidence level, assuming a mass difference of 130 GeV between $\tilde \chi_2^0$ and $\tilde \chi_1^0$. Model-independent upper limits are also set on the cross section of processes beyond the Standard Model.
The expected exclusion contour at $95\%$ CL as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Masses within the contour are excluded.
The observed exclusion contour at $95\%$ CL as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Masses within the contour are excluded.
Acceptance in the Single-bin SR as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.
Efficiency in the Single-bin SR as a function of the M(Sbottom) vs. M(N2) with the $\Delta$ M(N2,N1) $= 130$ GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.
Acceptance in the Multi-bin SR, $\min_{\Theta} < 0.5$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.
Efficiency in the Multi-bin SR, $\min_{\Theta} < 0.5$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.
Acceptance in the Multi-bin SR, $0.5 < \min_{\Theta} < 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.
Efficiency in the Multi-bin SR, $0.5 < \min_{\Theta} < 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.
Acceptance in the Multi-bin SR, $\min_{\Theta} > 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.
Efficiency in the Multi-bin SR, $\min_{\Theta} > 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.
Observed upper limits on the signal cross section as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV.
Expected upper limits on the signal cross section as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV.
Cutflows for the bechmarl signal point M(Sbottom) = 800 GeV, M(N2) = 180 GeV. Weighted event yields are reported starting with the "Preselection" line, normalized to an integrated luminosity of $139$ fb$^{−1}$.
Comparison of the expected and observed event yields in the signal regions. The top-quark and Z(mumu) background contributions are scaled with the normalization factors obtained from the background-only fit. The other contribution includes all the backgrounds not explicitly listed in the legend (V+jets except Z(mumu)+jets, di-/triboson, multijet). The hatched band indicates the total statistical and systematic uncertainties in the SM background. The contributions from three signal models to the signal regions are also displayed, where the masses M(Sbottom) and M(N2) are given in GeV in the legend. The lower panel shows the significance of the deviation of the observed yield from the expected background yield.
Dominant systematic uncertainties in the background prediction for the signal regions after the fit to the control regions. “Other” includes the uncertainties arising from muons, jet-vertex tagging, modeling of pile-up, the $E_{T}^{miss}$ computation, multijet background, and luminosity. The individual uncertainties can be correlated and do not necessarily add up quadratically to the total uncertainty.
A search for production of the supersymmetric partners of the top quark, top squarks, is presented. The search is based on proton-proton collision events containing multiple jets, no leptons, and large transverse momentum imbalance. The data were collected with the CMS detector at the CERN LHC at a center-of-mass energy of 13 TeV, and correspond to an integrated luminosity of 137 fb$^{-1}$. The targeted signal production scenarios are direct and gluino-mediated top squark production, including scenarios in which the top squark and neutralino masses are nearly degenerate. The search utilizes novel algorithms based on deep neural networks that identify hadronically decaying top quarks and W bosons, which are expected in many of the targeted signal models. No statistically significant excess of events is observed relative to the expectation from the standard model, and limits on the top squark production cross section are obtained in the context of simplified supersymmetric models for various production and decay modes. Exclusion limits as high as 1310 GeV are established at the 95% confidence level on the mass of the top squark for direct top squark production models, and as high as 2260 GeV on the mass of the gluino for gluino-mediated top squark production models. These results represent a significant improvement over the results of previous searches for supersymmetry by CMS in the same final state.
Top quark tagging efficiencies are shown as a function of the generator-level top quark $p_T$ for the merged tagging algorithm and resolved tagging algorithm described in the paper. This plot shows the efficiencies as calculated in a sample of simulated $t\bar{t}$ events in which one top quark decays leptonically, while the other decays hadronically. In addition to the individual algorithms shown as orange squares (boosted top quarks) and green inverted triangles (resolved top quarks), the total top quark tagging efficiency (blue dots) is also shown.
W boson tagging efficiencies are shown as a function of the generator-level W boson $p_T$ for the merged tagging algorithm described in the paper. This plot shows the W boson tagging efficiency when calculated in a sample of simulated WW events.
Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $p_T^{miss}$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.
The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $p_T^{miss}$ after scaling the simulation to match the total yield in data.
Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_t$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.
The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_t$ after scaling the simulation to match the total yield in data.
Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_W$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.
The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_W$ after scaling the simulation to match the total yield in data.
Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_{\text{res}}$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.
The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_{\text{res}}$ after scaling the simulation to match the total yield in data.
Observed event yields in data (black points) and predicted SM background (filled histograms) for the low $\Delta$m search bins 0--52. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.
The ratio of the data to the total background prediction for the low $\Delta$m search bins 0--52. The hatched bands correspond to the total uncertainty in the background prediction.
Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 53--104. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.
The ratio of the data to the total background prediction for the high $\Delta$m search bins 53--104. The hatched bands correspond to the total uncertainty in the background prediction.
Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 105--152 with ${N_b = 2}$. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.
The ratio of the data to the total background prediction for the high $\Delta$m search bins 105--152 with ${N_b = 2}$. The hatched bands correspond to the total uncertainty in the background prediction.
Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 153--182 with ${N_b \geq 3}$. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.
The ratio of the data to the total background prediction for the high $\Delta$m search bins 153--182 with ${N_b \geq 3}$. The hatched bands correspond to the total uncertainty in the background prediction.
The observed 95% CL upper limit on the production cross section of the T2tt simplified model as a function of the top squark and LSP masses. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.
The expected 95% CL upper limit on the production cross section of the T2tt simplified model as a function of the top squark and LSP masses. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.
The observed exclusion contour of the T2tt simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$). No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.
The mean expected exclusion contour of the T2tt simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.
The observed 95% CL upper limit on the production cross section of the T2bW simplified model as a function of the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2bW simplified model as a function of the top squark and LSP masses.
The observed exclusion contour of the T2bW simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2bW simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T2tb simplified model as a function of the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2tb simplified model as a function of the top squark and LSP masses.
The observed exclusion contour of the T2tb simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2tb simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T2ttC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2ttC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The observed exclusion contour of the T2ttC simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2ttC simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T2bWC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2bWC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The observed exclusion contour of the T2bWC simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2bWC simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T2cc simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The expected 95% CL upper limit on the production cross section of the T2cc simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.
The observed exclusion contour of the T2cc simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T2cc simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T1tttt simplified model as a function of the gluino and LSP masses.
The expected 95% CL upper limit on the production cross section of the T1tttt simplified model as a function of the gluino and LSP masses.
The observed exclusion contour of the T1tttt simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T1tttt simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T1ttbb simplified model as a function of the gluino and LSP masses.
The expected 95% CL upper limit on the production cross section of the T1ttbb simplified model as a function of the gluino and LSP masses.
The observed exclusion contour of the T1ttbb simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).
The mean expected exclusion contour of the T1ttbb simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.
The observed 95% CL upper limit on the production cross section of the T5ttcc simplified model as a function of the gluino and LSP masses. The upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.
The expected 95% CL upper limit on the production cross section of the T5ttcc simplified model as a function of the gluino and LSP masses. The uppser limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.
The observed exclusion contour of the T5ttcc simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$). The expected and observed upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.
The mean expected exclusion contour of the T5ttcc simplified model and the region containing 68% and 95% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis. The expected and observed upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.
The results of a search for direct pair production of top squarks and for dark matter in events with two opposite-charge leptons (electrons or muons), jets and missing transverse momentum are reported, using 139 fb$^{-1}$ of integrated luminosity from proton-proton collisions at $\sqrt{s} = 13$ TeV, collected by the ATLAS detector at the Large Hadron Collider during Run 2 (2015-2018). This search considers the pair production of top squarks and is sensitive across a wide range of mass differences between the top squark and the lightest neutralino. Additionally, spin-0 mediator dark-matter models are considered, in which the mediator is produced in association with a pair of top quarks. The mediator subsequently decays to a pair of dark-matter particles. No significant excess of events is observed above the Standard Model background, and limits are set at 95% confidence level. The results exclude top squark masses up to about 1 TeV, and masses of the lightest neutralino up to about 500 GeV. Limits on dark-matter production are set for scalar (pseudoscalar) mediator masses up to about 250 (300) GeV.
Two-body selection. Distributions of $m_{T2}$ in $SR^{2-body}_{110,\infty}$ for (a) different-flavour and (b) same-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference dark-matter signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction.
Two-body selection. Distributions of $m_{T2}$ in $SR^{2-body}_{110,\infty}$ for (a) different-flavour and (b) same-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference dark-matter signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction.
Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.
Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.
Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.
Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.
Four-body selection. (a) distributions of $E_{T}^{miss}$ in $SR^{4-body}_{Small\,\Delta m}$ and (b) distribution of $R_{2\ell 4j}$ in $SR^{4-body}_{Large\,\Delta m}$ for events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panel indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.
Four-body selection. (a) distributions of $E_{T}^{miss}$ in $SR^{4-body}_{Small\,\Delta m}$ and (b) distribution of $R_{2\ell 4j}$ in $SR^{4-body}_{Large\,\Delta m}$ for events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panel indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the Observed limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.
Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.
Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.
Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.
Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.
Two-body selection. Background fit results for $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}}$, $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}Z}$, $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t}, DF}$, $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t}, SF}$ and $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t} Z}$. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Three-body selection. Background fit results for $\mathrm{CR}^{\mathrm{3-body}}_{t\bar{t}}$, $\mathrm{CR}^{\mathrm{3-body}}_{VV}$, $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}Z}$, $\mathrm{VR}^{\mathrm{3-body}}_{VV}$, $\mathrm{VR(1)}^{\mathrm{3-body}}_{t\bar{t}}$ and $\mathrm{VR(2)}^{\mathrm{3-body}}_{t\bar{t}}$. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Four-body selection. Background fit results for $\mathrm{CR}^{\mathrm{4-body}}_{t\bar{t}}$,$\mathrm{CR}^{\mathrm{4-body}}_{VV}$, $\mathrm{VR}^{\mathrm{4-body}}_{t\bar{t}}$, $VR^{4-body}_{VV}$ and $\mathrm{VR}^{\mathrm{4-body}}_{VV,lll}$. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Two-body selection. Background fit results for the different-flavour leptons binned SRs. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Two-body selection. Background fit results for the same-flavour leptons binned SRs. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Three-body selection. Observed event yields and background fit results for the three-body selection SRs. The ''Others'' category contains contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Four-body selection. Observed event yields and background fit results for SR$^{\mathrm{4-body}}_{\mathrm{Small}\,\Delta m}$ and SR$^{\mathrm{4-body}}_{\mathrm{Large}\,\Delta m}$. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Exclusion limits contours (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with 100% branching ratio in $\tilde{t}_1--\tilde{\chi}^0_1$ masses planes. The dashed lines and the shaded bands are the expected limit and its $\pm 1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The exclusion limits contours for the two-body, three-body and four-body selections are respectively shown in blue, green and red.
Exclusion limits contours (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with 100% branching ratio in $\tilde{t}_1--\tilde{\chi}^0_1$ masses planes. The dashed lines and the shaded bands are the expected limit and its $\pm 1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The exclusion limits contours for the two-body, three-body and four-body selections are respectively shown in blue, green and red.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm 1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty.The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty.The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.
Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.
Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.
Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.
Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross-section to the nominal cross-section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines shows the observed (expected) exclusion limits.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Four-body selection Efficiency (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.
Four-body selection Efficiency (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta\ m}$ for a simplified model assuming $\tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} +\phi$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.
Four-body selection acceptance (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.
Four-body selection acceptance (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.
Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.
Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.
Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.
Three-body selection The numbers indicate the upper limits on the signal strenght for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.
Four-body selection The numbers indicate the upper limits on the signal strenght for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.
Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.
Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.
Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.
Three-body selection The numbers indicate the upper limits on the signal cross-section for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.
Four-body selection The numbers indicate the upper limits on the signal cross-section for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.
Two-body selection. Background fit results for the $inclusive$ SRs. The Others category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Note that the individual uncertainties can be correlated, and do not necessarily add up quadratically to the total background uncertainty.
Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=600~ GeV$ and $m(\tilde{\chi}^0_1)=400~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the scalar signal model $t\bar{t} + \phi $ with $m(\phi)=150~ GeV$ and $m(\chi)=1~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the pseudoscalar signal model $t\bar{t} + a $ with $m(a)=150~ GeV$ and $m(\chi)=1~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=385~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=400~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=430~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=460~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=400~ GeV$ and $m(\tilde{\chi}^0_1)=380~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=460~ GeV$ and $m(\tilde{\chi}^0_1)=415~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=400~ GeV$ and $m(\tilde{\chi}^0_1)=320~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.
The results of a search for gluino and squark pair production with the pairs decaying via the lightest charginos into a final state consisting of two $W$ bosons, the lightest neutralinos ($\tilde\chi^0_1$), and quarks, are presented. The signal is characterised by the presence of a single charged lepton ($e^{\pm}$ or $\mu^{\pm}$) from a $W$ boson decay, jets, and missing transverse momentum. The analysis is performed using 139 fb$^{-1}$ of proton-proton collision data taken at a centre-of-mass energy $\sqrt{s}=13$ TeV delivered by the Large Hadron Collider and recorded by the ATLAS experiment. No statistically significant excess of events above the Standard Model expectation is found. Limits are set on the direct production of squarks and gluinos in simplified models. Masses of gluino (squark) up to 2.2 TeV (1.4 TeV) are excluded at 95% confidence level for a light $\tilde\chi^0_1$.
Post-fit $m_{T}$ distribution in the SR 2J b-veto N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 2J b-veto N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 2J b-tag N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 2J b-tag N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 4J b-veto N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 4J b-veto N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 4J b-tag N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 4J b-tag N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 6J b-veto N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 6J b-veto N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 6J b-tag N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{T}$ distribution in the SR 6J b-tag N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Pre-fit $m_{eff}$ distribution in the TR6J control region. Uncertainties include statistical and systematic uncertainties (added in quadrature). The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 2J b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Pre-fit $m_{eff}$ distribution in the WR6J control region. Uncertainties include statistical and systematic uncertainties (added in quadrature). The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 2J b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the TR6J control region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 4J low-x b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the WR6J control region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 4J low-x b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 2J b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 4J high-x b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 2J b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 4J high-x b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 4J low-x b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 6J b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 4J low-x b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 6J b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 4J high-x b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Observed 95% CL exclusion contours for the gluino one-step x = 1/2 model.
Post-fit $m_{eff}$ distribution in the 4J high-x b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Expected 95% CL exclusion contours for the gluino one-step x = 1/2 model. space.
Post-fit $m_{eff}$ distribution in the 6J b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Observed 95% CL exclusion contours for the gluino one-step variable-x
Post-fit $m_{eff}$ distribution in the 6J b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.
Expected 95% CL exclusion contours for the gluino one-step variable-x
Observed 95% CL exclusion contours for the gluino one-step x = 1/2 model.
Observed 95% CL exclusion contours for the squark one-step x = 1/2 model.
Expected 95% CL exclusion contours for the gluino one-step x = 1/2 model. space.
Observed 95% CL exclusion contours for the squark one-step x = 1/2 model.
Observed 95% CL exclusion contours for the gluino one-step variable-x
Observed 95% CL exclusion contours for one-flavour schemes in one-step x = 1/2 model.
Expected 95% CL exclusion contours for the gluino one-step variable-x
Observed 95% CL exclusion contours for one-flavour schemes in one-step x = 1/2 model.
Observed 95% CL exclusion contours for the squark one-step x = 1/2 model.
Expected 95% CL exclusion contours for the squark one-step variable-x
Observed 95% CL exclusion contours for the squark one-step x = 1/2 model.
Expected 95% CL exclusion contours for the squark one-step variable-x
Observed 95% CL exclusion contours for one-flavour schemes in one-step x = 1/2 model.
Expected 95% CL exclusion contours for the squark one-flavour schemes in variable-x
Observed 95% CL exclusion contours for one-flavour schemes in one-step x = 1/2 model.
Expected 95% CL exclusion contours for the squark one-flavour schemes in variable-x
Expected 95% CL exclusion contours for the squark one-step variable-x
Upper limits on the signal cross section for simplified model gluino one-step x = 1/2
Expected 95% CL exclusion contours for the squark one-step variable-x
Upper limits on the signal cross section for simplified model gluino one-step variable-x
Expected 95% CL exclusion contours for the squark one-flavour schemes in variable-x
Upper limits on the signal cross section for simplified model squark one-step x = 1/2
Expected 95% CL exclusion contours for the squark one-flavour schemes in variable-x
Upper limits on the signal cross section for simplified model squark one-step variable-x
Upper limits on the signal cross section for simplified model gluino one-step x = 1/2
Upper limits on the signal cross section for simplified model squark one-step x=1/2 in one-flavour schemes
Upper limits on the signal cross section for simplified model gluino one-step variable-x
Upper limits on the signal cross section for simplified model squark one-step variable-x in one-flavour schemes
Upper limits on the signal cross section for simplified model squark one-step x = 1/2
Post-fit $m_{eff}$ distribution in the 2J b-tag validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Upper limits on the signal cross section for simplified model squark one-step variable-x
Post-fit $m_{eff}$ distribution in the 2J b-veto validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Upper limits on the signal cross section for simplified model squark one-step x=1/2 in one-flavour schemes
Post-fit $m_{eff}$ distribution in the 4J b-tag validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Upper limits on the signal cross section for simplified model squark one-step variable-x in one-flavour schemes
Post-fit $m_{eff}$ distribution in the 4J b-veto validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the TR2J control region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 6J b-tag validation region. Uncertainties include statistical and systematic uncertainties.
Post-fit $m_{eff}$ distribution in the WR2J control region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Post-fit $m_{eff}$ distribution in the 6J b-veto validation region. Uncertainties include statistical and systematic uncertainties.
Post-fit $m_{eff}$ distribution in the TR4J control region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Event selection cutflow for two representative signal samples for the SR2JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Post-fit $m_{eff}$ distribution in the WR4J control region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Event selection cutflow for two representative signal samples for the SR2JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Post-fit $m_{eff}$ distribution in the 2J b-tag validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Event selection cutflow for two representative signal samples for the SR4JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Post-fit $m_{eff}$ distribution in the 2J b-veto validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Event selection cutflow for two representative signal samples for the SR4JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Post-fit $m_{eff}$ distribution in the 4J b-tag validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Event selection cutflow for two representative signal samples for the SR6JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Post-fit $m_{eff}$ distribution in the 4J b-veto validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.
Event selection cutflow for two representative signal samples for the SR6JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Post-fit $m_{eff}$ distribution in the 6J b-tag validation region. Uncertainties include statistical and systematic uncertainties.
Signal acceptance in SR2J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models
Post-fit $m_{eff}$ distribution in the 6J b-veto validation region. Uncertainties include statistical and systematic uncertainties.
Signal acceptance in SR2J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models
Event selection cutflow for two representative signal samples for the SR2JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Signal acceptance in SR2J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models
Event selection cutflow for two representative signal samples for the SR2JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Signal acceptance in SR2J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models
Event selection cutflow for two representative signal samples for the SR4JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Signal acceptance in SR2J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models
Event selection cutflow for two representative signal samples for the SR4JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Signal acceptance in SR2J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models
Event selection cutflow for two representative signal samples for the SR6JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Signal acceptance in SR2J discovery high region for gluino production one-step x = 1/2 simplified models
Event selection cutflow for two representative signal samples for the SR6JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.
Signal acceptance in SR2J discovery low region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx discovery region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J discovery high region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J discovery low region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx discovery region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx discovery region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx discovery region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin4 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin4 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J discovery high region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J discovery low region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin4 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin4 region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J discovery high region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J discovery high region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J discovery low region for gluino production one-step x = 1/2 simplified models
Signal acceptance in SR2J discovery low region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx discovery region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J discovery high region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J discovery low region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx discovery region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx discovery region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx discovery region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin4 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin4 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J discovery high region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J discovery low region for gluino production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin4 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin1 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin2 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin3 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin4 region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J discovery high region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J discovery high region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J discovery low region for gluino production one-step variable-x simplified models
Signal acceptance in SR2J discovery low region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx discovery region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J discovery high region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J discovery low region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx discovery region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx discovery region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Tag bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jhx b-Veto bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx discovery region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin4 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Tag bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR4Jlx b-Veto bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Veto bin4 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J discovery high region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J discovery low region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR6J b-Tag bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin4 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin1 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Tag bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin2 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin3 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin4 region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J b-Veto bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J discovery high region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J discovery high region for squark production one-step variable-x simplified models
Signal acceptance in SR6J discovery low region for squark production one-step x = 1/2 simplified models
Signal acceptance in SR2J discovery low region for squark production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx discovery region for squark production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR2J b-Tag bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR2J b-Veto bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR2J discovery high region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR2J discovery low region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx discovery region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx discovery region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Tag bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jhx b-Veto bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx discovery region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin4 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Tag bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR4Jlx b-Veto bin3 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Veto bin4 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin1 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J discovery high region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin2 region for squark production one-step variable-x simplified models
Signal acceptance in SR6J discovery low region for squark production one-step variable-x simplified models
Signal acceptance in SR6J b-Tag bin3 region for squark production one-step variable-x simplified models
Signal efficiency in SR2J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal acceptance in SR6J b-Tag bin4 region for squark production one-step variable-x simplified models
Signal efficiency in SR2J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal acceptance in SR6J b-Veto bin1 region for squark production one-step variable-x simplified models
Signal efficiency in SR2J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal acceptance in SR6J b-Veto bin2 region for squark production one-step variable-x simplified models
Signal efficiency in SR2J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal acceptance in SR6J b-Veto bin3 region for squark production one-step variable-x simplified models
Signal efficiency in SR2J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal acceptance in SR6J b-Veto bin4 region for squark production one-step variable-x simplified models
Signal efficiency in SR2J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal acceptance in SR6J discovery high region for squark production one-step variable-x simplified models
Signal efficiency in SR2J discovery high region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal acceptance in SR6J discovery low region for squark production one-step variable-x simplified models
Signal efficiency in SR2J discovery low region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx discovery region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery high region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery low region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx discovery region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx discovery region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx discovery region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin4 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin4 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery high region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery low region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin4 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin4 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery high region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery high region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery low region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery low region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx discovery region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery high region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery low region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx discovery region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx discovery region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx discovery region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin4 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin4 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery high region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery low region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin4 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin4 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery high region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery high region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery low region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery low region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx discovery region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery high region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery low region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx discovery region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx discovery region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx discovery region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin4 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin4 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery high region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery low region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin4 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin4 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery high region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery high region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery low region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery low region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx discovery region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery high region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR2J discovery low region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx discovery region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx discovery region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jhx b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx discovery region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin4 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR4Jlx b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin4 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery high region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery low region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Tag bin4 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J b-Veto bin4 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery high region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
Signal efficiency in SR6J discovery low region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0
A search is presented for new phenomena in events characterised by high jet multiplicity, no leptons (electrons or muons), and four or more jets originating from the fragmentation of $b$-quarks ($b$-jets). The search uses 139 fb$^{-1}$ of $\sqrt{s}$ = 13 TeV proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider during Run 2. The dominant Standard Model background originates from multijet production and is estimated using a data-driven technique based on an extrapolation from events with low $b$-jet multiplicity to the high $b$-jet multiplicities used in the search. No significant excess over the Standard Model expectation is observed and 95% confidence-level limits that constrain simplified models of R-parity-violating supersymmetry are determined. The exclusion limits reach 950 GeV in top-squark mass in the models considered.
Results of a search for new particles decaying into eight or more jets and moderate missing transverse momentum are presented. The analysis uses 139 fb$^{-1}$ of proton$-$proton collision data at $\sqrt{s} = 13$ TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. The selection rejects events containing isolated electrons or muons, and makes requirements according to the number of $b$-tagged jets and the scalar sum of masses of large-radius jets. The search extends previous analyses both in using a larger dataset and by employing improved jet and missing transverse momentum reconstruction methods which more cleanly separate signal from background processes. No evidence for physics beyond the Standard Model is found. The results are interpreted in the context of supersymmetry-inspired simplified models, significantly extending the limits on the gluino mass in those models. In particular, limits on the gluino mass are set at 2 TeV when the lightest neutralino is nearly massless in a model assuming a two-step cascade decay via the lightest chargino and second-lightest neutralino.
Post-fit yields for data and prediction in each of the multi-bin signal regions for the 8 jet regions.
Post-fit yields for data and prediction in each of the multi-bin signal regions for the 9 jet regions.
Post-fit yields for data and prediction in each of the multi-bin signal regions for the 10 jet regions.
Post-fit yields for data and prediction in each of the single-bin signal regions of the analysis.
Observed 95% confidence level limit for the two-step signal grid.
Observed 95% confidence level limit for the two-step signal grid with the signal cross section increased by one sigma.
Observed 95% confidence level limit for the two-step signal grid with the signal cross section decreased by one sigma.
Expected 95% confidence level limit for the two-step signal grid.
Expected 95% confidence level limit for the two-step signal grid plus one sigma from experimental systematics.
Expected 95% confidence level limit for the two-step signal grid minus one sigma from experimental systematics.
Observed 95% confidence level limit for the Gtt signal grid.
Observed 95% confidence level limit for the Gtt signal grid with the signal cross section increased by one sigma.
Observed 95% confidence level limit for the Gtt signal grid with the signal cross section decreased by one sigma.
Expected 95% confidence level limit for the Gtt signal grid.
Expected 95% confidence level limit for the Gtt signal grid plus one sigma from experimental systematics.
Expected 95% confidence level limit for the Gtt signal grid minus one sigma from experimental systematics.
Observed 95% confidence level limit for the RPV signal grid.
Observed 95% confidence level limit for the RPV signal grid with the signal cross section increased by one sigma.
Observed 95% confidence level limit for the RPV signal grid with the signal cross section decreased by one sigma.
Expected 95% confidence level limit for the RPV signal grid.
Expected 95% confidence level limit for the RPV signal grid plus one sigma from experimental systematics.
Expected 95% confidence level limit for the RPV signal grid minus one sigma from experimental systematics.
Observed 95% confidence level limit for the two-step signal grid.
Expected 95% confidence level limit for the two-step signal grid.
Observed 95% confidence level limit for the Gtt signal grid.
Expected 95% confidence level limit for the Gtt signal grid.
Observed 95% confidence level limit for the RPV signal grid.
Expected 95% confidence level limit for the RPV signal grid.
$\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in the signal region SR-10ij50-0ib-MJ340. Two benchmark signal models are shown along with the background yields. These models, each representing a single mass point, are labelled 'RPV' with $(m_{\tilde{g}}, m_{\tilde{t}}) = (1600, 600) \, \mathrm{GeV}$ and 'two-step' with $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
$\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in the signal region SR-12ij50-2ib. Two benchmark signal models are shown along with the background yields. These models, each representing a single mass point, are labelled 'RPV' with $(m_{\tilde{g}}, m_{\tilde{t}}) = (1600, 600) \, \mathrm{GeV}$ and 'two-step' with $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
$\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in the signal region SR-9ij80-0ib. Two benchmark signal models are shown along with the background yields. These models, each representing a single mass point, are labelled 'RPV' with $(m_{\tilde{g}}, m_{\tilde{t}}) = (1600, 600) \, \mathrm{GeV}$ and 'two-step' with $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-8ij50-0ib-MJ500. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-9ij50-0ib-MJ340. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-10ij50-0ib-MJ340. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-10ij50-0ib-MJ500. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-10ij50-1ib-MJ500. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-11ij50-0ib. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-12ij50-2ib. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Number of signal events expected for $139 \, \mathrm{fb}^{-1} $ after different analysis selections in the signal region SR-9ij80-0ib. This 'two-step' model requires that a strongly produced gluino decays into quarks, the W and Z bosons, and the lightest stable neutralino where $(m_{\tilde{g}}, m_{\tilde{\chi^{0}_{1}}}) = (1600, 100) \, \mathrm{GeV}$.
Acceptance for the signal region SR-8ij50-0ib-MJ500 showing the acceptance for the complete two-step signal grid.
Efficiency for the signal region SR-8ij50-0ib-MJ500 showing the efficiency for the complete two-step signal grid.
Acceptance for the signal region SR-9ij50-0ib-MJ340 showing the acceptance for the complete two-step signal grid.
Efficiency for the signal region SR-9ij50-0ib-MJ340 showing the efficiency for the complete two-step signal grid.
Acceptance for the signal region SR-10ij50-0ib-MJ340 showing the acceptance for the complete two-step signal grid.
Efficiency for the signal region SR-10ij50-0ib-MJ340 showing the efficiency for the complete two-step signal grid.
Acceptance for the signal region SR-10ij50-0ib-MJ500 showing the acceptance for the complete two-step signal grid.
Efficiency for the signal region SR-10ij50-0ib-MJ500 showing the efficiency for the complete two-step signal grid.
Acceptance for the signal region SR-10ij50-1ib-MJ500 showing the acceptance for the complete two-step signal grid.
Efficiency for the signal region SR-10ij50-1ib-MJ500 showing the efficiency for the complete two-step signal grid.
Acceptance for the signal region SR-11ij50-0ib showing the acceptance for the complete two-step signal grid.
Efficiency for the signal region SR-11ij50-0ib showing the efficiency for the complete two-step signal grid.
Acceptance for the signal region SR-12ij50-2ib showing the acceptance for the complete two-step signal grid.
Efficiency for the signal region SR-12ij50-2ib showing the efficiency for the complete two-step signal grid.
Acceptance for the signal region SR-9ij80-0ib showing the acceptance for the complete two-step signal grid.
Efficiency for the signal region SR-9ij80-0ib showing the efficiency for the complete two-step signal grid.
The normalisation factors for the dominant backgrounds of the analysis in each of the multi-bin and single-bin regions.
Post-fit yields for data and prediction in each of the single-bin validation regions to test the $N_{\mathrm{jet}}$ extraction.
Post-fit yields for data and prediction in each of the single-bin validation regions to test the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ extrapolation.
Post-fit yields for data and prediction in each of the multi-bin validation regions to test the $N_{\mathrm{jet}}$ extraction.
Post-fit yields for data and prediction in each of the multi-bin validation regions to test the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ extrapolation.
The observed Cls from the best expected signal regions for the two-step decay.
The observed Cls from the best expected signal regions for the Gtt decay.
The observed Cls from the best expected signal regions for the RPV decay.
Number of events in each signal region broken down by background type and the number of observed data events.
From left to right; the $95\%$ CL upper limits on the visible cross section (${\langle \epsilon\sigma \rangle}^{95}_{obs}$) and on the number of signal events. Next is the $95\%$ CL upper limit on the number of signal events, given the expected number of background events. The last two columns show the confidence level for the background only hypothesis ($CL_{b}$) and the dicovery $p$-value along with the Gaussian significance (Z).
Visualisation of the highest jet multiplicity event selected in signal regions targeting long cascade decays of pair-produced gluinos. This event was recorded by ATLAS on 23 October 2016, and contains 16 jets, illustrated by cones. Yellow blocks represent the calorimeter energy measured in noise-suppressed clusters. Of the reconstructed jets, 13 (11) have transverse momenta above 50 GeV (80 GeV), with 3 (2) being b-tagged. The leading jet has a transverse momentum of 507 GeV, and the sum of jet transverse momenta $H_T=2.9$ TeV. A value of 343 GeV is observed for the $E_{T}^{miss}$, whose direction is shown by the dashed red line, producing a significance $S(E_{T}^{miss})=6.4$. The sum of the masses of large-radius jets is evaluated as $M_{J}^{\Sigma}=1070$ GeV.
Visualisation of the highest jet multiplicity event selected in a control region used to make predictions of the background from multijet production. This event was recorded by ATLAS on 18 July 2018, and contains 19 jets, illustrated by cones. Yellow blocks represent the calorimeter energy measured in in noise-suppressed clusters. Of the reconstructed jets, 16 (10) have transverse momenta above 50 GeV (80 GeV). No jets were b-tagged. The leading et has a transverse momentum of 371 GeV, and the sum of jet transverse momenta $H_T=2.2$ TeV. A value of 8 GeV is observed for the $E_{T}^{miss}$, whose direction is shown by the dashed red line, producing a significance $S(E_{T}^{miss})=0.2$. The sum of the masses of large-radius jets is evaluated as $M_{J}^{\Sigma}=767$ GeV.
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