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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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$^{-1}$.
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.
Post-fit signal region yields for the tt0L-high and the tt0L-low analyses. The bottom panel shows the statistical significance of the difference between the SM prediction and the observed data in each region. '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.
Representative fit distribution in the signal region for the tt1L analysis: each bin of such distribution corresponds to a single SR included in the fit. 'Other' includes contributions from $t\bar{t}W$, $tZ$, $tWZ$ and $t\bar{t}$ (semileptonic) processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.
Representative fit distribution in the same flavour leptons signal region for the tt2L analysis: each bin of such distribution, starting from the red arrow, corresponds to a single SR included in the fit. 'FNP' includes the contribution from fake/non-prompt lepton background arising from jets (mainly $\pi/K$, heavy-flavour hadron decays and photon conversion) misidentified as leptons, estimated in a purely data-driven way. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.
Summary of the total uncertainty in the background prediction for each SR of the tt0L-low, tt0L-high, tt1L and tt2L analysis channels in the statistical combination. Their dominant contributions are indicated by individual lines. Individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.
Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.
$E_{\text{T}}^{\text{miss}}$ distribution in SR0X for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.
$E_{\text{T}}^{\text{miss}}$ distribution in SRWX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.
$E_{\text{T}}^{\text{miss}}$ distribution in SRTX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.
Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. 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 show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.
Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. 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 show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.
Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.
Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.
Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. 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 show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.
Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. 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 show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.
Representative fit distribution in the different flavour leptons signal region for the tt2L analysis: each bin of such distribution, starting from the red arrow, corresponds to a single SR included in the fit. 'FNP' includes the contribution from fake/non-prompt lepton background arising from jets (mainly $\pi/K$, heavy-flavour hadron decays and photon conversion) misidentified as leptons, estimated in a purely data-driven way. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.
Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.
Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.
Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.
Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of selected reconstructed events divided by the acceptance.
Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of selected reconstructed events divided by the acceptance.
Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of selected reconstructed events divided by the acceptance.
Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
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].
Three searches are presented for signatures of physics beyond the standard model (SM) in $\tau\tau$ final states in proton-proton collisions at the LHC, using a data sample collected with the CMS detector at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Upper limits at 95% confidence level (CL) are set on the products of the branching fraction for the decay into $\tau$ leptons and the cross sections for the production of a new boson $\phi$, in addition to the H(125) boson, via gluon fusion (gg$\phi$) or in association with b quarks, ranging from $\mathcal{O}$(10 pb) for a mass of 60 GeV to 0.3 fb for a mass of 3.5 TeV each. The data reveal two excesses for gg$\phi$ production with local $p$-values equivalent to about three standard deviations at $m_\phi$ = 0.1 and 1.2 TeV. In a search for $t$-channel exchange of a vector leptoquark U$_1$, 95% CL upper limits are set on the dimensionless U$_1$ leptoquark coupling to quarks and $\tau$ leptons ranging from 1 for a mass of 1 TeV to 6 for a mass of 5 TeV, depending on the scenario. In the interpretations of the $M_\mathrm{h}^{125}$ and $M_\mathrm{h, EFT}^{125}$ minimal supersymmetric SM benchmark scenarios, additional Higgs bosons with masses below 350 GeV are excluded at 95% CL.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled. The peak in the expected $gg\phi$ limit is tribute to a loss of sensitivity around $90\text{ GeV}$ due to the background from $Z/\gamma^\ast\rightarrow\tau\tau$ events. Numerical values provided in this table correspond to Figure 10a of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $bb\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $gg\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 10b of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 37 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $bb\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $gg\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 38 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled and only top quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 39 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled and only bottom quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 40 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 31 of the auxilliary material of the publication.
Local significance for a $bb\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $gg\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 32 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 33 of the auxilliary material of the publication.
Local significance for a $bb\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $gg\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 34 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled and only top quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 35 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled and only bottom quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 36 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$), via vector boson fusion ($qq\phi$) or in association with b quarks ($bb\phi$). In this case, $bb\phi$ production rate is profiled, whereas the scan is performed in the $gg\phi$ and $qq\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 64 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $60\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 65 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $60\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 66 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $80\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 67 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $80\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 68 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $95\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 69 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $95\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 70 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $100\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 71 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $100\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 72 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $120\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 73 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $120\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 74 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $125\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 75 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $125\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 76 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $130\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 77 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $130\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 78 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $140\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 79 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $140\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 80 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $160\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 81 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $160\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 82 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $180\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 83 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $180\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 84 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $200\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 85 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $200\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 86 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 1 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 12a of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 2 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 12b of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 3 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 92 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $60\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11a of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $80\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 41 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 42 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $100\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11b of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $120\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 43 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $125\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11c of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $130\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 44 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $140\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 45 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $160\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11d of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $180\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 46 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 47 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $250\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11e of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 48 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $350\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 49 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 50 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $450\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 51 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11f of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 52 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $700\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 53 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 54 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 55 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11g of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11h of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 56 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 57 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 58 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 59 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 60 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 61 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 62 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 63 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11i of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $60\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11a of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $80\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 41 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 42 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $100\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11b of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $120\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 43 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $125\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11c of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $130\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 44 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $140\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 45 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $160\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11d of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $180\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 46 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 47 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $250\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11e of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 48 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $350\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 49 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 50 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $450\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 51 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11f of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 52 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $700\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 53 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 54 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 55 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11g of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11h of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 56 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 57 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 58 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 59 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 60 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 61 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 62 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 63 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11i of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 99 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 100 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 101 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 102 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 103 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 104 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 105 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 106 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 107 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 108 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 109 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 110 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 111 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 112 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 113 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ quantile contour of Figure 13a of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ quantile contour of Figure 13b of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 114 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 115 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 116 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 117 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 118 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 119 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario. Numerical values provided in this table correspond to the observed contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 120 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 122 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 123 of the auxilliary material of the publication.
Fractions of the cross-section $\sigma(gg\phi)$ as expected from SM for the loop contributions with only top quarks, only bottom quarks and from the top-bottom interference. These values are used to scale the corresponding signal components for a given mass $m_\phi$.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
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 $p_{\mathrm{T}}^{e\mu}$
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