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Several extensions of the Standard Model predict the production of dark matter particles at the LHC. A search for dark matter particles produced in association with a dark Higgs boson decaying into $W^{+}W^{-}$ in the $\ell^\pm\nu q \bar q'$ final states with $\ell=e,\mu$ is presented. This analysis uses 139 fb$^{-1}$ of $pp$ collisions recorded by the ATLAS detector at a centre-of-mass energy of 13 TeV. The $W^\pm \to q\bar q'$ decays are reconstructed from pairs of calorimeter-measured jets or from track-assisted reclustered jets, a technique aimed at resolving the dense topology from a pair of boosted quarks using jets in the calorimeter and tracking information. The observed data are found to agree with Standard Model predictions. Scenarios with dark Higgs boson masses ranging between 140 and 390 GeV are excluded.
Probability of finding at least one TAR jet, where the p<sub>T</sub>-leading TAR jet passes the m<sub>Wcand</sub> and D<sub>2</sub><sup>β=1</sup> requirements, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=500 GeV, with the preselections applied.
Probability of finding at least one TAR jet, where the p<sub>T</sub>-leading TAR jet passes the m<sub>Wcand</sub> and D<sub>2</sub><sup>β=1</sup> requirements, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=1000 GeV, with the preselections applied.
Probability of finding at least one TAR jet, where the p<sub>T</sub>-leading TAR jet passes the m<sub>Wcand</sub> and D<sub>2</sub><sup>β=1</sup> requirements, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=1700 GeV, with the preselections applied.
Probability of finding at least one TAR jet, where the p<sub>T</sub>-leading TAR jet passes the m<sub>Wcand</sub> and D<sub>2</sub><sup>β=1</sup> requirements, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=2100 GeV, with the preselections applied.
Probability of finding a W<sub>had</sub> candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=500 GeV, with the preselections applied that do not pass the requirements of the merged category.
Probability of finding a W<sub>had</sub> candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=1000 GeV, with the preselections applied that do not pass the requirements of the merged category.
Probability of finding a W<sub>had</sub> candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=1700 GeV, with the preselections applied that do not pass the requirements of the merged category.
Probability of finding a W<sub>had</sub> candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=2100 GeV, with the preselections applied that do not pass the requirements of the merged category.
Observed exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected+1σ exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected-1σ exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected+2σ exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected-2σ exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) for m<sub>Z'</sub>=0.5 TeV as a function of m<sub>s</sub>. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) process for m<sub>Z'</sub>=0.5 TeV, shown in dashed blue.
Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) for m<sub>Z'</sub>=1 TeV as a function of m<sub>s</sub>. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) process for m<sub>Z'</sub>=1 TeV, shown in dashed blue.
Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) for m<sub>Z'</sub>=1.7 TeV as a function of m<sub>s</sub>. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) process for m<sub>Z'</sub>=1.7 TeV, shown in dashed blue.
Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) for m<sub>Z'</sub>=2.1 TeV as a function of m<sub>s</sub>. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) process for m<sub>Z'</sub>=2.1 TeV, shown in dashed blue.
Data overlaid on SM background yields stacked in each SR and CR category after the fit to data ('Post-fit'). The yields in the SR are broken down into their contributions to the individual bins. The maximum-likelihood estimators are set to the conditional values of the CR-only fit, and propagated to SR and CRs.
Dominant sources of uncertainty for three dark Higgs scenarios after the fit to data. The uncertainties are quantified in terms of their contribution to the fitted signal uncertainty that is expressed relative to the theory prediction. Three representative dark Higgs signal scenarios with g<sub>q</sub>=0.25, g<sub>χ</sub>=1.0, sinθ=0.01 and m<sub>χ</sub>=200 GeV are considered, which are indicated using the (m<sub>Z'</sub>, m<sub>s</sub>) format in units of GeV in the table columns.
Cumulative efficiencies in the merged category for three representative dark Higgs signal scenarios with g<sub>q</sub>=0.25, g<sub>&chi</sub>;=1.0, sinθ=0.01, m<sub>Z'</sub> = 1 TeV, and m<sub>χ</sub>=200 GeV considering s→W(ℓν)W(qq) decays only.
Cumulative efficiencies in the resolved category for three representative dark Higgs signal scenarios with g<sub>q</sub>=0.25, g<sub>&chi</sub>;=1.0, sinθ=0.01, m<sub>Z'</sub> = 1 TeV, and m<sub>χ</sub>=200 GeV considering s→W(ℓν)W(qq) decays only.
Theoretical cross section for σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) for each of the dark Higgs signal points at m<sub>Z′</sub> ={300, 350, 400, 500, 750} GeV, with g<sub>q</sub> = 0.25, g<sub>χ = 1.0, sinθ = 0.01, m<sub>Z′</sub> = 1 TeV , and m<sub>χ</sub> = 200 GeV. Also shown are experimentally excluded cross sections of σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (±1σ).
Theoretical cross section for σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) for each of the dark Higgs signal points at m<sub>Z′</sub> ={1000, 1700} GeV, with g<sub>q</sub> = 0.25, g<sub>χ = 1.0, sinθ = 0.01, m<sub>Z′</sub> = 1 TeV , and m<sub>χ</sub> = 200 GeV. Also shown are experimentally excluded cross sections of σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (±1σ).
Theoretical cross section for σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) for each of the dark Higgs signal points at m<sub>Z′</sub> ={2100, 2500, 2900, 3300} GeV, with g<sub>q</sub> = 0.25, g<sub>χ = 1.0, sinθ = 0.01, m<sub>Z′</sub> = 1 TeV , and m<sub>χ</sub> = 200 GeV. Also shown are experimentally excluded cross sections of σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (±1σ).
The $e^+e^-\to K^+K^-$ cross section and charged-kaon electromagnetic form factor are measured in the $e^+e^-$ center-of-mass energy range ($E$) from 2.6 to 8.0 GeV using the initial-state radiation technique with an undetected photon. The study is performed using 469 fb$^{-1}$ of data collected with the BABAR detector at the PEP-II $e^+e^-$ collider at center-of-mass energies near 10.6 GeV. The form factor is found to decrease with energy faster than $1/E^2$, and approaches the asymptotic QCD prediction. Production of the $K^+K^-$ final state through the $J/\psi$ and $\psi(2S)$ intermediate states is observed. The results for the kaon form factor are used together with data from other experiments to perform a model-independent determination of the relative phases between single-photon and strong amplitudes in $J/\psi$ and $\psi(2S)\to K^+K^-$ decays. The values of the branching fractions measured in the reaction $e^+e^- \to K^+K^-$ are shifted relative to their true values due to interference between resonant and nonresonant amplitudes. The values of these shifts are determined to be about $\pm5\%$ for the $J/\psi$ meson and $\pm15\%$ for the $\psi(2S)$ meson.
The $K^+K^-$ invariant-mass interval ($M_{K^+K^-}$), number of selected events ($N_{\rm sig}$) after background subtraction, detection efficiency ($\varepsilon$), ISR luminosity ($L$), measured $e^+e^-\to K^+K^-$ cross section ($\sigma_{K^+K^-}$), and the charged-kaon form factor ($|F_K|$). For the number of events and cross section. For the form factor, we quote the combined uncertainty. For the mass interval 7.5 - 8.0 GeV/$c^2$, the 90$\%$ CL upper limits for the cross section and form factor are listed.
The results of a search for top squark (stop) pair production in final states with one isolated lepton, jets, and missing transverse momentum are reported. The analysis is performed with proton--proton collision data at $\sqrt{s} = 8$ TeV collected with the ATLAS detector at the LHC in 2012 corresponding to an integrated luminosity of $20$ fb$^{-1}$. The lightest supersymmetric particle (LSP) is taken to be the lightest neutralino which only interacts weakly and is assumed to be stable. The stop decay modes considered are those to a top quark and the LSP as well as to a bottom quark and the lightest chargino, where the chargino decays to the LSP by emitting a $W$ boson. A wide range of scenarios with different mass splittings between the stop, the lightest neutralino and the lightest chargino are considered, including cases where the $W$ bosons or the top quarks are off-shell. Decay modes involving the heavier charginos and neutralinos are addressed using a set of phenomenological models of supersymmetry. No significant excess over the Standard Model prediction is observed. A stop with a mass between $210$ and $640$ GeV decaying directly to a top quark and a massless LSP is excluded at $95$ % confidence level, and in models where the mass of the lightest chargino is twice that of the LSP, stops are excluded at $95$ % confidence level up to a mass of $500$ GeV for an LSP mass in the range of $100$ to $150$ GeV. Stringent exclusion limits are also derived for all other stop decay modes considered, and model-independent upper limits are set on the visible cross-section for processes beyond the Standard Model.
Expected and observed $H_{T,sig}^{miss}$ distribution for tN_med SR, before applying the $H_{T,sig}^{miss}>12$ requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed large-R jet mass distribution for tN_boost SR, before applying the large-R jet mass$>75$ GeV requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed b-jet multiplicity distribution for bCc_diag SR, before applying the b-jet multiplicity$=0$ requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed $am_{T2}$ distribution for bCd_high1 SR, before applying the $am_{T2}>200$ GeV requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed leading b-jet $p_T$ distribution for bCd_high2 SR, before applying the leading b-jet $p_T>170$ GeV requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed $E_T^{miss}$ distribution for tNbC_mix SR, before applying the $E_T^{miss}>270$ GeV requirement. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed lepton $p_T$ distribution for bCa_low SR. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed lepton $p_T$ distribution for bCa_med SR. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed $am_T2$ distribution for bCb_med1 SR. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Expected and observed $am_T2$ distribution for bCb_high SR. The uncertainty includes statistical and all experimental systematic uncertainties. The last bin includes overflows.
Best expected signal region for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1$ three-body scenario ($\tilde t_1\to bW\chi^0_1$). This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1$ four-body scenario ($\tilde t_1\to bff'\chi^0_1$). This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=150$ GeV. This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=106$ GeV. This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+5$ GeV. This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\tilde t_1}-10$ GeV. This mapping is used for the final combined exclusion limits.
Best expected signal region for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\tilde t_1}=300$ GeV. This mapping is used for the final combined exclusion limits.
Upper limits on the model cross-section for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Observed exclusion contour for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Expected exclusion contour for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Upper limit on signal events for the $\tilde t_1$ three-body scenario ($\tilde t_1\to bW\chi^0_1$).
Observed exclusion contour for the $\tilde t_1$ three-body scenario ($\tilde t_1\to bW\chi^0_1$).
Expected exclusion contour for the $\tilde t_1$ three-body scenario ($\tilde t_1\to bW\chi^0_1$).
Upper limit on signal events for the $\tilde t_1$ four-body scenario ($\tilde t_1\to bff'\chi^0_1$).
Observed exclusion contour for the $\tilde t_1$ four-body scenario ($\tilde t_1\to bff'\chi^0_1$).
Expected exclusion contour for the $\tilde t_1$ four-body scenario ($\tilde t_1\to bff'\chi^0_1$).
Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=150$ GeV.
Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=150$ GeV.
Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=150$ GeV.
Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=106$ GeV.
Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=106$ GeV.
Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=106$ GeV.
Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+5$ GeV.
Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+5$ GeV.
Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+5$ GeV.
Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\tilde t_1}-10$ GeV.
Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\tilde t_1}-10$ GeV.
Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\tilde t_1}-10$ GeV.
Upper limit on signal events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\tilde t_1}=300$ GeV.
Observed exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\tilde t_1}=300$ GeV.
Expected exclusion contour for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\tilde t_1}=300$ GeV.
Acceptance of tN_diag SR ($E_T^{miss}>150$ GeV, $m_T>140$ GeV) for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of tN_med SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of tN_boost SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCb_med2 SR ($am_{T2}>250$ GeV, $m_T>60$ GeV) for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCc_diag SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCd_bulk SR ($am_{T2}>175$ GeV, $m_T>120$ GeV) for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCd_high1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCd_high2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCa_med for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCa_low for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCb_med1 for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of bCb_high for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of 3-body SR ($80<am_{T2}<90$ GeV, $m_T>120$ GeV) for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$). The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Acceptance of tNbC_mix SR for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.
Efficiency of tN_diag SR ($E_T^{miss}>150$ GeV, $m_T>140$ GeV) for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of tN_med SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of tN_boost SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCb_med2 SR ($am_{T2}>250$ GeV, $m_T>60$ GeV) for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCc_diag SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCd_bulk SR ($am_{T2}>175$ GeV, $m_T>120$ GeV) for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCd_high1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCd_high2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCa_med for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCa_low for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCb_med1 for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of bCb_high for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of 3-body SR ($80<am_{T2}<90$ GeV, $m_T>120$ GeV) for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$). The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Efficiency of tNbC_mix SR for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).
Number of generated events for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Number of generated events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Number of generated events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV; $E_T^{miss}$(gen)$>60$ GeV.
Number of generated events for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV; $E_T^{miss}$(gen)$>250$ GeV.
Number of generated events for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$).
Number of generated events for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Cross-section for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Cross-section for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Cross-section for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Cross-section for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$).
Cross-section for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Combined experimental systematic uncertainty of expected tN_diag SR yields for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$, using the 2 highest $E_T^{miss}$ and 2 highest $m_T$ bins.
Combined experimental systematic uncertainty of expected tN_med SR yields for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Combined experimental systematic uncertainty of expected tN_boost SR yields for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Combined experimental systematic uncertainty of expected bCb_med2 SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$, using the 2 highest $am_{T2}$ and 2 highest $m_T$ bins.
Combined experimental systematic uncertainty of expected bCc_diag SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Combined experimental systematic uncertainty of expected bCd_bulk SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$, using the 2 highest $am_{T2}$ and 2 highest $m_T$ bins.
Combined experimental systematic uncertainty of expected bCd_high1 SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Combined experimental systematic uncertainty of expected bCd_high2 SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Combined experimental systematic uncertainty of expected bCa_med SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Combined experimental systematic uncertainty of expected bCa_low SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Combined experimental systematic uncertainty of expected bCb_med1 SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Combined experimental systematic uncertainty of expected bCb_high SR yields for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Combined experimental systematic uncertainty of expected 3-body SR yields for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$), using the 2 lowest $am_{T2}$ and 2 highest $m_T$ bins.
Combined experimental systematic uncertainty of expected tNbC_mix SR yields for the asymmetric scenario ($\tilde t_1$, $\tilde t_1\to t\chi^0_1$, b $\chi^\pm_1$) with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Observed CLs in tN_diag SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Observed CLs in tN_med SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Observed CLs in tN_boost SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Observed CLs in bCb_med2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Observed CLs in bCc_diag SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Observed CLs in bCd_bulk SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Observed CLs in bCd_high1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Observed CLs in bCd_high2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Observed CLs in bCa_med SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Observed CLs in bCa_low SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Observed CLs in bCb_med1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Observed CLs in bCb_high SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Observed CLs in 3-body SR for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$).
Observed CLs in tNbC_mix SR for the mixed scenario (50% $\tilde t_1\to t\chi^0_1$, 50% $\tilde t_1\to b\chi^0_1$).
Expected CLs in tN_diag SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Expected CLs in tN_med SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Expected CLs in tN_boost SR for the $\tilde t_1\to t\chi^0_1$ scenario with $m_{\tilde t_1}>m_t+m_{\chi^0_1}$.
Expected CLs in bCb_med2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Expected CLs in bCc_diag SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Expected CLs in bCd_bulk SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Expected CLs in bCd_high1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Expected CLs in bCd_high2 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=2\times m_{\chi^0_1}$.
Expected CLs in bCa_med SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Expected CLs in bCa_low SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Expected CLs in bCb_med1 SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Expected CLs in bCb_high SR for the $\tilde t_1\to b\chi^\pm_1$ scenario with $m_{\chi^\pm_1}=m_{\chi^0_1}+20$ GeV.
Expected CLs in 3-body SR for the 3-body scenario ($\tilde t_1\to b W\chi^0_1$).
Expected CLs in tNbC_mix SR for the mixed scenario (50% $\tilde t_1\to t\chi^0_1$, 50% $\tilde t_1\to b\chi^\pm_1$).
A search is presented for the direct pair production of a chargino and a neutralino $pp\to\tilde{\chi}^\pm_1\tilde{\chi}^0_2$, where the chargino decays to the lightest neutralino and the $W$ boson, $\tilde{\chi}^\pm_1 \to \tilde{\chi}^0_1 (W^{\pm}\to\ell^{\pm}\nu)$, while the neutralino decays to the lightest neutralino and the 125 GeV Higgs boson, $\tilde{\chi}^0_2 \to \tilde{\chi}^0_1 (h\to bb/\gamma\gamma/\ell^{\pm}\nu qq)$. The final states considered for the search have large missing transverse momentum, an isolated electron or muon, and one of the following: either two jets identified as originating from bottom quarks, or two photons, or a second electron or muon with the same electric charge. The analysis is based on 20.3 fb$^{-1}$ of $\sqrt{s}=8$ TeV proton-proton collision data delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with the Standard Model expectations, and limits are set in the context of a simplified supersymmetric model.
Distribution of contransverse mass $m_{\rm CT}$ in CRlbb-T, central $m_{bb}$ bin. The background histograms are obtained from the background-only fit, and their uncertainty represents the total background uncertainty after the fit. The last bin includes overflow.
Distribution of contransverse mass $m_{\rm CT}$ in SRlbb-1 and SRlbb-2, $m_{bb}$ sideband. The background histograms are obtained from the background-only fit, and their uncertainty represents the total background uncertainty after the fit. The last bin includes overflow.
Distribution of the transverse mass of the $W$-candidate $m_{\rm T}^{W}$ for the one lepton and two $b$-jets channel in VRlbb-2, central $m_{bb}$ bin. The background histograms are obtained from the background-only fit, and their uncertainty represents the total background uncertainty after the fit. The last bin includes overflow.
Distribution of the transverse mass of the $W$-candidate $m_{\rm T}^{W}$ for the one lepton and two $b$-jets channel in SRlbb-1 and SRlbb-2, $m_{bb}$ sidebands. The background histograms are obtained from the background-only fit, and their uncertainty represents the total background uncertainty after the fit. The last bin includes overflow.
Distribution of the number of $b$-jets for the one lepton and two $b$-jets channel in SRlbb-1 and SRlbb-2 without the b-jet multiplicity requirement, central $m_{bb}$ bin. The background histograms are obtained from the background-only fit, and their uncertainty represents the total background uncertainty after the fit.
Distribution of the invariant mass of the $b$-jets $m_{bb}$ for the one lepton and two $b$-jets channel in the SRlbb-1 and SRlbb-2. The background histograms are obtained from the background-only fit, and their uncertainty represents the total background uncertainty after the fit.
Distribution of missing transverse momentum $E_{\rm T}^{\rm miss}$ in the one lepton and two photons signal regions SR$\ell\gamma\gamma$-1 and SR$\ell\gamma\gamma$-2 for the Higgs-mass window ($120\lt m_{\gamma\gamma} \lt 130$ GeV). The final column (background) is a simulation-based cross check. The contributions from non-Higgs backgrounds are scaled by 10 GeV / 50 GeV = 0.2 from the $m_{\gamma\gamma}$ sideband ($100 \lt m_{\gamma\gamma} \lt 120$ GeV and $130 \lt m_{\gamma\gamma} \lt 160$ GeV) into the Higgs-mass window. The last bin includes overflow. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(165,35)$ GeV.
Distribution of the azimuth difference between the $W$ and Higgs boson candidates $\Delta\phi(W,h)$ in the one lepton and two photons signal regions SR$\ell\gamma\gamma$-1 and SR$\ell\gamma\gamma$-2 for the Higgs-mass window ($120\lt m_{\gamma\gamma} \lt 130$ GeV). The final column (background) is a simulation-based cross check. The contributions from non-Higgs backgrounds are scaled by 10 GeV / 50 GeV = 0.2 from the $m_{\gamma\gamma}$ sideband ($100 \lt m_{\gamma\gamma} \lt 120$ GeV and $130 \lt m_{\gamma\gamma} \lt 160$ GeV) into the Higgs-mass window. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(165,35)$ GeV.
Distribution of the transverse mass of the $W$ and photon system $m_{\rm{T}}^{W\gamma_1}$ in the one lepton and two photons signal regions SR$\ell\gamma\gamma$-1 and SR$\ell\gamma\gamma$-2 for the Higgs-mass window ($120\lt m_{\gamma\gamma} \lt 130$ GeV). The final column (background) is a simulation-based cross check. The contributions from non-Higgs backgrounds are scaled by 10 GeV / 50 GeV = 0.2 from the $m_{\gamma\gamma}$ sideband ($100 \lt m_{\gamma\gamma} \lt 120$ GeV and $130 \lt m_{\gamma\gamma} \lt 160$ GeV) into the Higgs-mass window. The last bin includes overflow. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(165,35)$ GeV.
Distribution of the transverse mass of the $W$ and photon system $m_{\rm{T}}^{W\gamma_2}$ in the one lepton and two photons signal regions SR$\ell\gamma\gamma$-1 and SR$\ell\gamma\gamma$-2 for the Higgs-mass window ($120\lt m_{\gamma\gamma} \lt 130$ GeV). The final column (background) is a simulation-based cross check. The contributions from non-Higgs backgrounds are scaled by 10 GeV / 50 GeV = 0.2 from the $m_{\gamma\gamma}$ sideband ($100 \lt m_{\gamma\gamma} \lt 120$ GeV and $130 \lt m_{\gamma\gamma} \lt 160$ GeV) into the Higgs-mass window. The last bin includes overflow. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(165,35)$ GeV.
Results of the background-only fit to the diphoton invariant mass, $m_{\gamma\gamma}$, distribution in the one lepton and two photons signal region SR$l\gamma\gamma$-1. The contributions from SM Higgs boson production are constrained to the MC prediction and associated systematic uncertainties. The fit is performed on events with 100 GeV $ \lt m_{\gamma\gamma} \lt $ 160 GeV, with events in SR$l\gamma\gamma$-1 or SR$l\gamma\gamma$-2 in the Higgs-mass window (120 GeV $\le m_{\gamma\gamma} \le$ 130 GeV) excluded from the fit.
Results of the background-only fit to the diphoton invariant mass, $m_{\gamma\gamma}$, distribution in the one lepton and two photons signal region SR$l\gamma\gamma$-2. The contributions from SM Higgs boson production are constrained to the MC prediction and associated systematic uncertainties. The fit is performed on events with 100 GeV $ \lt m_{\gamma\gamma} \lt $ 160 GeV, with events in SR$l\gamma\gamma$-1 or SR$l\gamma\gamma$-2 in the Higgs-mass window (120 GeV $\le m_{\gamma\gamma} \le$ 130 GeV) excluded from the fit.
Results of the background-only fit to the diphoton invariant mass, $m_{\gamma\gamma}$, distribution in the one lepton and two photons validation region VR$l\gamma\gamma$-1. The contributions from SM Higgs boson production are constrained to the MC prediction and associated systematic uncertainties. The fit is performed on events with 100 GeV $ \lt m_{\gamma\gamma} \lt $ 160 GeV, with events in SR$l\gamma\gamma$-1 or SR$l\gamma\gamma$-2 in the Higgs-mass window (120 GeV $\le m_{\gamma\gamma} \le$ 130 GeV) excluded from the fit.
Results of the background-only fit to the diphoton invariant mass, $m_{\gamma\gamma}$, distribution in the one lepton and two photons validation region VR$l\gamma\gamma$-2. The contributions from SM Higgs boson production are constrained to the MC prediction and associated systematic uncertainties. The fit is performed on events with 100 GeV $ \lt m_{\gamma\gamma} \lt $ 160 GeV, with events in SR$l\gamma\gamma$-1 or SR$l\gamma\gamma$-2 in the Higgs-mass window (120 GeV $\le m_{\gamma\gamma} \le$ 130 GeV) excluded from the fit.
Distribution of effective mass $m_{\rm eff}$ in the validation region of the same-sign $e\mu$ channel. This validation region is defined by requiring one, two, or three jets, and reversing the $m_{lj}$, $m_{ljj}$ criteria. The distribution of a signal hypothesis is also shown.
Distribution of effective mass $m_{\rm eff}$ for the same-sign dilepton channel in the signal regions with one jet SR$ll$-1. SR$ll$-1 is the sum of SR$ee$-1, SR$e\mu$-1, and SR$\mu\mu$-1. All selection criteria are applied, except for the one on $m_{\rm eff}$. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(130,0)$ GeV. The last bin includes overflow.
Distribution of effective mass $m_{\rm eff}$ for the same-sign dilepton channel in the signal regions with two or three jets SR$ll$-2. SR$ll$-2 is the sum of SR$ee$-2, SR$e\mu$-2, and SR$\mu\mu$-2. All selection criteria are applied, except for the one on $m_{\rm eff}$. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(130,0)$ GeV. The last bin includes overflow.
Distribution of largest transverse mass $m_{\rm T}^{\rm max}$ for the same-sign dilepton channel in the signal regions with one jet SR$ll$-1. SR$ll$-1 is the sum of SR$ee$-1, SR$e\mu$-1, and SR$\mu\mu$-1. All selection criteria are applied, except for the one on $m_{\rm T}^{\rm max}$. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(130,0)$ GeV. The last bin includes overflow.
Distribution of largest transverse mass $m_{\rm T}^{\rm max}$ for the same-sign dilepton channel in the signal regions with two or three jets SR$ll$-2. SR$ll$-2 is the sum of SR$ee$-2, SR$e\mu$-2, and SR$\mu\mu$-2. All selection criteria are applied, except for the one on $m_{\rm T}^{\rm max}$. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(130,0)$ GeV. The last bin includes overflow.
Distribution of invariant mass of lepton and jet $m_{lj}$ for the same-sign dilepton channel in the signal regions with one jet SR$ll$-1. SR$ll$-1 is the sum of SR$ee$-1, SR$e\mu$-1, and SR$\mu\mu$-1. All selection criteria are applied, except for the one on $m_{lj}$. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(130,0)$ GeV. The last bin includes overflow.
Distribution of invariant mass of lepton and di-jet $m_{ljj}$ for the same-sign dilepton channel in the signal regions with two or three jets SR$ll$-2. SR$ll$-2 is the sum of SR$ee$-2, SR$e\mu$-2, and SR$\mu\mu$-2. All selection criteria are applied, except for the one on $m_{ljj}$. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(130,0)$ GeV. The last bin includes overflow.
One lepton and two $b$-jets channel: observed and expected 95% CL upper limits on the cross section normalised by the simplified model prediction as a function of the common mass $m_{\tilde{\chi}_1^\pm \tilde{\chi}^0_2}$ for $m_{\tilde{\chi}^0_1}=0$.
One lepton and two photons channel: observed and expected 95% CL upper limits on the cross section normalised by the simplified model prediction as a function of the common mass $m_{\tilde{\chi}_1^\pm \tilde{\chi}^0_2}$ for $m_{\tilde{\chi}^0_1}=0$.
Same-sign dilepton channel: observed and expected 95% CL upper limits on the cross section normalised by the simplified model prediction as a function of the common mass $m_{\tilde{\chi}_1^\pm \tilde{\chi}^0_2}$ for $m_{\tilde{\chi}^0_1}=0$.
Combination: observed and expected 95% CL upper limits on the cross section normalised by the simplified model prediction as a function of the common mass $m_{\tilde{\chi}_1^\pm \tilde{\chi}^0_2}$ for $m_{\tilde{\chi}^0_1}=0$. This combination is obtained using the result from the ATLAS three-lepton search, J. High Energy Phys. 04 (2014) 169, in addition to the three channels reported in this paper.
One lepton and two b-jets channel: Expected 95% CL exclusion contour for chargino neutralino production via Wh.
One lepton and two b-jets channel: Observed 95% CL exclusion contour for chargino neutralino production via Wh.
One lepton and two photons channel: Expected 95% CL exclusion contour for chargino neutralino production via Wh.
One lepton and two photons channel: Observed 95% CL exclusion contour for chargino neutralino production via Wh.
Same-sign dilepton channel: Expected 95% CL exclusion contour for chargino neutralino production via Wh.
Same-sign dilepton channel: Observed 95% CL exclusion contour for chargino neutralino production via Wh.
Combination: Expected 95% CL exclusion contour for chargino neutralino production via Wh.
Combination: Observed 95% CL exclusion contour for chargino neutralino production via Wh.
Combination: excluded model cross-section at 95% CL IN PB.
Acceptance SR$\ell\gamma\gamma$-1.
Acceptance SR$\ell\gamma\gamma$-2.
Efficiency SR$\ell\gamma\gamma$-1.
Efficiency SR$\ell\gamma\gamma$-2.
$m_{\gamma\gamma}$ distribution in the SR$l\gamma\gamma$-1 region for the full $m_{\gamma\gamma}$ window. The last column (background) is from a simulation-based cross check. $Z\gamma$ events, with $Z\rightarrow{ee}$ and one electron mis-identified as a photon, are included in the $Z\gamma(\gamma)$ background. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(165,35)$ GeV.
$m_{\gamma\gamma}$ distribution in the SR$l\gamma\gamma$-2 region for the full $m_{\gamma\gamma}$ window. The last column (background) is from a simulation-based cross check. $Z\gamma$ events, with $Z\rightarrow{ee}$ and one electron mis-identified as a photon, are included in the $Z\gamma(\gamma)$ background. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(165,35)$ GeV.
$m_{\gamma\gamma}$ distribution in the VR$l\gamma\gamma$-1 region for the full $m_{\gamma\gamma}$ window. The last column (background) is from a simulation-based cross check. $Z\gamma$ events, with $Z\rightarrow{ee}$ and one electron mis-identified as a photon, are included in the $Z\gamma(\gamma)$ background. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(165,35)$ GeV.
$m_{\gamma\gamma}$ distribution in the VR$l\gamma\gamma$-2 region for the full $m_{\gamma\gamma}$ window. The last column (background) is from a simulation-based cross check. $Z\gamma$ events, with $Z\rightarrow{ee}$ and one electron mis-identified as a photon, are included in the $Z\gamma(\gamma)$ background. The distributions of a signal hypothesis are also shown for $m(\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2},\tilde{\chi}^{0}_{1})=(165,35)$ GeV.
Acceptance for SRlbb-1, central $m_{bb}$ bin.
Acceptance for SRlbb-1, $m_{bb}$ sideband.
Acceptance for SRlbb-2, central $m_{bb}$ bin.
Acceptance for SRlbb-2, $m_{bb}$ sideband.
Efficiency for SRlbb-1, central $m_{bb}$ bin.
Efficiency for SRlbb-1, $m_{bb}$ sideband.
Efficiency for SRlbb-2, central $m_{bb}$ bin.
Efficiency for SRlbb-2, $m_{bb}$ sideband.
Acceptance for the same-sign $ee$ channel with one jet.
Acceptance for the same-sign $ee$ channel with two or three jets.
Acceptance for the same-sign $e\mu$ channel with one jet.
Acceptance for the same-sign $e\mu$ channel with two or three jets.
Acceptance for the same-sign $\mu\mu$ channel with one jet.
Acceptance for the same-sign $\mu\mu$ channel with two or three jets.
Efficiency for the same-sign $ee$ channel with one jet.
Efficiency for the same-sign $ee$ channel with two or three jets.
Efficiency for the same-sign $e\mu$ channel with one jet.
Efficiency for the same-sign $e\mu$ channel with two or three jets.
Efficiency for the same-sign $\mu\mu$ channel with one jet.
Efficiency for the same-sign $\mu\mu$ channel with two or three jets.
A search for dark matter in events with a displaced nonresonant muon pair and missing transverse momentum is presented. The analysis is performed using an integrated luminosity of 138 fb$^{-1}$ of proton-proton (pp) collision data at a center-of-mass energy of 13 TeV produced by the LHC in 2016-2018. No significant excess over the predicted backgrounds is observed. Upper limits are set on the product of the inelastic dark matter production cross section $\sigma$(pp $\to$ A' $\to$$\chi_1$$\chi_2$) and the decay branching fraction $\mathcal{B}$($\chi_2$$\to$$\chi_1 \mu^+ \mu^-$), where A' is a dark photon and $\chi_1$ and $\chi_2$ are states in the dark sector with near mass degeneracy. This is the first dedicated collider search for inelastic dark matter.
Definition of ABCD bins and yields in data, per match category. The predicted yield in the bin with the smallest backgrounds (bin D) is extracted from the simultaneous four-bin fit by assuming zero signal, which corresponds to $(\text{Obs. B} \times \text{Obs. C}) / (\text{Obs. A})$ in this limit.
Systematic uncertainties in the analysis. The jet uncertainties are larger in 2017 because of noise issues with the ECAL endcap. The tracking inefficiency in 2016 is caused by the unexpected saturation of photodiode signals in the tracker.
Simulated muon reconstruction efficiency of standard (blue squares) and displaced (red circles) reconstruction algorithms as a function of transverse vertex displacement $v_{xy}$. The two dashed vertical gray lines denote the ends of the fiducial tracker and muon detector regions, respectively.
Measured min-$d_{xy}$ distribution in the 2-match category, after requiring the $d_{xy}$ muon to pass the isolation requirement $I_{\mathrm{rel}}^{\mathrm{PF}} <0.25$ (i. e., the B and D bins of the ABCD plane). Overlaid with a red histogram is the background predicted from the region of the ABCD plane failing the same requirement (the A and C bins), as well as three signal benchmark hypotheses (as defined in the legends), assuming $\alpha_D = \alpha_{\mathrm{EM}}$ (the fine-structure constant). The red hatched bands correspond to the background prediction uncertainty. The last bin includes the overflow.
Two-dimensional exclusion surfaces for $\Delta = 0.1 \, m_1$ as a function of the DM mass $m_1$ and the signal strength $y$, with $m_{A'} = 3 \, m_1$. Filled histograms denote observed limits on $\sigma(\mathrm{pp} \to A' \to \chi_2 \chi_1) \, \mathcal{B}(\chi_2 \to \chi_1 \mu^+ \mu^-)$. Solid (dashed) curves denote the observed (expected) exclusion limits at 95% CL, with 68% CL uncertainty bands around the expectation. Regions above the curves are excluded, depending on the $\alpha_D$ hypothesis: $\alpha_{\mathrm{D}} = \alpha_{\mathrm{EM}}$ (dark blue) or 0.1 (light magenta).
Two-dimensional exclusion surfaces for $\Delta = 0.4 \, m_1$ as a function of the DM mass $m_1$ and the signal strength $y$, with $m_{A'} = 3 \, m_1$. Filled histograms denote observed limits on $\sigma(\mathrm{pp} \to A' \to \chi_2 \chi_1) \, \mathcal{B}(\chi_2 \to \chi_1 \mu^+ \mu^-)$. Solid (dashed) curves denote the observed (expected) exclusion limits at 95% CL, with 68% CL uncertainty bands around the expectation. Regions above the curves are excluded, depending on the $\alpha_D$ hypothesis: $\alpha_{\mathrm{D}} = \alpha_{\mathrm{EM}}$ (dark blue) or 0.1 (light magenta).
A search for strongly produced supersymmetric particles is conducted using signatures involving multiple energetic jets and either two isolated leptons ($e$ or $\mu$) with the same electric charge, or at least three isolated leptons. The search also utilises jets originating from b-quarks, missing transverse momentum and other observables to extend its sensitivity. The analysis uses a data sample corresponding to a total integrated luminosity of 20.3 fb$^{-1}$ of $\sqrt{s} =$ 8 TeV proton-proton collisions recorded with the ATLAS detector at the Large Hadron Collider in 2012. No deviation from the Standard Model expectation is observed. New or significantly improved exclusion limits are set on a wide variety of supersymmetric models in which the lightest squark can be of the first, second or third generations, and in which R-parity can be conserved or violated.
Numbers of observed and background events for SR0b for each bin of the distribution in Meff. The table corresponds to Fig. 4(b). The statistical and systematic uncertainties are combined for the expected backgrounds.
Numbers of observed and background events for SR1b for each bin of the distribution in Meff. The table corresponds to Fig. 4(c). The statistical and systematic uncertainties are combined for the predicted numbers.
Numbers of observed and background events for SR3b for each bin of the distribution in Meff. The table corresponds to Fig. 4(a). The statistical and systematic uncertainties are combined for the predicted numbers.
Numbers of observed and background events for SR3L low for each bin of the distribution in Meff. The table corresponds to Fig. 4(d). The statistical and systematic uncertainties are combined for the predicted numbers.
Numbers of observed and background events for SR3L high for each bin of the distribution in Meff. The table corresponds to Fig. 4(e). The statistical and systematic uncertainties are combined for the predicted numbers.
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).
The efficiencies are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.
The efficiencies are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.
The efficiencies are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into b s and gluinos decay into t stop (see Fig. 5d in the paper).
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).
The efficiencies are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).
The efficiencies are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).
The acceptances (in percent, %) are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.
The acceptances (in percent, %) are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.
The acceptances (in percent, %) are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).
The acceptances (in percent, %) are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).
The acceptances (in percent, %) are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.
The limits on observed cross section are calculated for all simplified models. The simplified models are for direct pair production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair-production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).
The limits on observed cross sections are calculated for all simplified models. The simplified models are for direct pair production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).
The signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.
The signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop)-20 GeV.
The signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).
The signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).
The signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).
Experimental uncertainties on the signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.
Experimental uncertainties on the signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.
Experimental uncertainties on the signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).
Experimental uncertainties on the signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).
Experimental uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).
Statistical uncertainties on the signal event yields are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the values are given for the five signal regions and their combination.
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.
Statistical uncertainties on the signal event yields are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, and mu>0.
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.
Statistical uncertainties on the signal event yields are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).
Statistical uncertainties on the signal event yields are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the values are given for the five signal regions and their combination. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).
Statistical uncertainties on the signal event yields are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W ^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluino), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.
The confidence levels are calculated for all simplified models. For each model, the observed and expected values are given. The simplified model is for direct production of gluinos that decay into t tbar t tbar chi1^0 chi1^0 (see Fig. 5a in the paper). This particular model assumes that top quark is much heavier than gluino.
The confidence levels are calculated for all simplified models. For each model, the observed and expected values are given. The simplified model is for direct production of squarks that decay into two steps into q q W Z W Z chi1^0 chi1^0 (see Fig. 6c in the paper).
The confidence levels are calculated for all simplified models. For each model, the values are given for the five signal regions and their combination. The simplified model is for direct pair-production of gluinos that decay via a two-step process into q q q q W Z W Z chi1^0 chi1^0 (see Fig. 6b in the paper).
The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos that decay via sleptons into q q q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6d in the paper).
The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct pair-production of gluinos. A gluino decays into t stop. Consequently, a top squark squark decays into b chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 5b in the paper). This particular model assumes that m(stop) < m(gluion), m(chi1^0)=6 GeV, and m(chi1^(+-))=118 GeV.
The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos. A gluino decays into t c chi1^0 (see Fig. 5c in the paper). This particular model assumes that m(chi1^0) = m(stop) - 20 GeV.
The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7b in the paper). This particular model assumes that m(chi1^0)=2(chi1^0).
The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of bottom squarks. A bottom squark decays into t chi1^(+-) and chi1^(+-) --> W^(+-) chi1^0 (see Fig. 7a in the paper). This particular model assumes that m(chi1^0)=60 GeV.
The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of squarks. Squarks decay into q q l l (l l) chi1^0 chi1^0 + neutrinos (see Fig. 6e in the paper).
The confidence levels are calculated for all GMSB models (see Fig. 8c in the paper). For each model, the expected and observed values are given. The model assumes mmess=250 TeV, m5=3, mu>0, and Cgrav=1.
The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos and top squarks. Top squarks undergo R-parity violating decays into bs and gluinos decay into t stop (see Fig. 5d in the paper).
The confidence levels are calculated for all mSUGRA/CMSSM models with bRPV (see Fig. 8b in the paper). For each model, the expected and observed values are given. The model assumes tan(beta)=30, A0=2m0, mu>0, and bRPV.
The confidence levels are calculated for all simplified extra dimension model (see Fig. 8d in the paper). For each model, the expected and observed values are given.
The confidence levels are calculated for all simplified models. For each model, the expected and observed values are given. The simplified model is for direct production of gluinos that decay into q q q q W W chi1^0 chi1^0 (see Fig. 6a in the paper).
The confidence levels are calculated for all mSUGRA models (see Fig. 8a in the paper). For each model, the expected and observed values are given. The model assumes tan(beta)=30, A0=2m0, and mu>0.
The results of a search for supersymmetry in final states containing at least one isolated lepton (electron or muon), jets and large missing transverse momentum with the ATLAS detector at the Large Hadron Collider (LHC) are reported. The search is based on proton-proton collision data at a centre-of-mass energy $\sqrt{s} = 8$ TeV collected in 2012, corresponding to an integrated luminosity of 20 fb$^{-1}$. No significant excess above the Standard Model expectation is observed. Limits are set on the parameters of a minimal universal extra dimensions model, excluding a compactification radius of $1/R_c=950$ GeV for a cut-off scale times radius ($\Lambda R_c$) of approximately 30, as well as on sparticle masses for various supersymmetric models. Depending on the model, the search excludes gluino masses up to 1.32 TeV and squark masses up to 840 GeV.
Observed and expected $E_T^{miss}/m_{eff}$ distribution in soft single-lepton 3-jet signal region. The last bin includes the overflow.
Observed and expected $E_T^{miss}/m_{eff}$ distribution in soft single-lepton 5-jet signal region. The last bin includes the overflow.
Observed and expected $E_T^{miss}/m_{eff}$ distribution in soft single-lepton 3-jet inclusive signal region. The last bin includes the overflow.
Observed and expected $E_T^{miss}$ distribution in soft dimuon signal region. The last bin includes the overflow.
Observed and expected $m_{eff}^{incl}$ distribution in hard single-lepton 3-jet signal region. The last bin includes the overflow.
Observed and expected $m_{eff}^{incl}$ distribution for hard single-lepton 5-jet signal region. The last bin includes the overflow.
Observed and expected $E_{T}^{miss}$ distribution for hard single-lepton 6-jet signal region. The last bin includes the overflow.
Observed and expected $M_{R}'$ distribution for hard same-flavour dilepton low-multiplicity signal region. The last bin includes the overflow.
Observed and expected $M_{R}'$ distribution for hard same-flavour dilepton 3-jet signal region. The last bin includes the overflow.
Observed and expected $M_{R}'$ distribution for hard opposite-flavour dilepton low-multiplicity signal region. The last bin includes the overflow.
Observed and expected $M_{R}'$ distribution for hard opposite-flavour dilepton 3-jet opposite-flavour signal region. The last bin includes the overflow.
Observed 95% exclusion contour for the mSUGRA/CMSSM model with $\tan\beta=30$, $A_{0}=-2m_{0}$ and $\mu > 0$.
Expected 95% exclusion contour for the mSUGRA/CMSSM model with $\tan\beta=30$, $A_{0}=-2m_{0}$ and $\mu > 0$.
Observed 95% exclusion contour for the bRPV MSUGRA/CMSSM model.
Expected 95% exclusion contour for the bRPV MSUGRA/CMSSM model.
Observed 95% exclusion contour for the natural gauge mediation with a stau NLSP model (nGM).
Expected 95% exclusion contour for the natural gauge mediation with a stau NLSP model (nGM).
Observed 95% exclusion contour for the non-universal higgs masses with gaugino mediation model (NUHMG).
Expected 95% exclusion contour for the non-universal higgs masses with gaugino mediation model (NUHMG).
Observed 95% exclusion contour for the minimal UED model from the combination of the hard dilepton and soft dilepton analyses.
Expected 95% exclusion contour for the minimal UED model from the combination of the hard dilepton and soft dilepton analyses.
Observed 95% exclusion contour for the minimal UED model from the hard dilepton analysis.
Expected 95% exclusion contour for the minimal UED model from the hard dilepton analysis.
Observed 95% exclusion contour for the minimal UED model from the soft dilepton analysis.
Expected 95% exclusion contour for the minimal UED model from the soft dilepton analysis.
Observed 95% exclusion contour for the simplified model with gluino-mediated top squark production where the top squark is assumed to decay exclusively via $\tilde{t} \rightarrow c \tilde{\chi}^{0}_{1}$.
Expected 95% exclusion contour for the simplified model with gluino-mediated top squark production, where the top squark is assumed to decay exclusively via $\tilde{t} \rightarrow c \tilde{\chi}^{0}_{1}$.
Observed 95% exclusion contour for the simplified model with gluino-mediated top squark production where the gluinos are assumed to decay exclusively through a virtual top squark, $\tilde{g} \rightarrow tt+\tilde{\chi}^{0}_{1}$.
Expected 95% exclusion contour for the simplified model with gluino-mediated top squark production where the gluinos are assumed to decay exclusively through a virtual top squark, $\tilde{g} \rightarrow tt+\tilde{\chi}^{0}_{1}$.
Observed 95% exclusion contour for the gluino simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Expected 95% exclusion contour for the gluino simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Observed 95% exclusion contour for the gluino simplified model from the hard single-lepton analyses for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Expected 95% exclusion contour for the gluino simplified model from the hard single-lepton analyses for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Observed 95% exclusion contour for the gluino simplified model from the soft single-lepton analyses for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Expected 95% exclusion contour for the gluino simplified model from the soft single-lepton analyses for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Observed 95% exclusion contour for the the first- and second-generation squark simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which the chargino mass is fixed at x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) = 1/2.
Expected 95% exclusion contour for the the first- and second-generation squark simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which the chargino mass is fixed at x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) = 1/2.
Observed 95% exclusion contour for the the first- and second-generation squark simplified model from the hard single-lepton analysis for the case in which the chargino mass is fixed at x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) = 1/2.
Expected 95% exclusion contour for the the first- and second-generation squark simplified model from the hard single-lepton analysis for the case in which the chargino mass is fixed at x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) = 1/2.
Observed 95% exclusion contour for the the first- and second-generation squark simplified model from the soft single-lepton analysis for the case in which the chargino mass is fixed at x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) = 1/2.
Expected 95% exclusion contour for the the first- and second-generation squark simplified model from the soft single-lepton analysis for the case in which the chargino mass is fixed at x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) = 1/2.
Observed 95% exclusion contour for the gluino simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Expected 95% exclusion contour for the gluino simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Observed 95% exclusion contour for the gluino simplified model from the hard single-lepton analysis for the case in which x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Expected 95% exclusion contour for the gluino simplified model from the hard single-lepton analysis for the case in which x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Observed 95% exclusion contour for the gluino simplified model from the soft single-lepton analysis for the case in which x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Expected 95% exclusion contour for the gluino simplified model from the soft single-lepton analysis for the case in which x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Observed 95% exclusion contour for the first- and second-generation squark simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Expected 95% exclusion contour for the first- and second-generation squark simplified model from the combination of soft single-lepton and hard single-lepton analyses for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Observed 95% exclusion contour for the first- and second-generation squark simplified model from the hard single-lepton analyses for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Expected 95% exclusion contour for the first- and second-generation squark simplified model from the hard single-lepton analyses for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Observed 95% exclusion contour for the first- and second-generation squark simplified model from the soft single-lepton analyses for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Expected 95% exclusion contour for the first- and second-generation squark simplified model from the soft single-lepton analyses for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Observed 95% exclusion contour for the two-step gluino simplified model with sleptons from the combination of the hard dilepton and hard single-lepton analyses.
Expected 95% exclusion contour for the two-step gluino simplified model with sleptons from the combination of the hard dilepton and hard single-lepton analyses.
Observed 95% exclusion contour for the two-step gluino simplified model with sleptons from the hard single-lepton analysis.
Expected 95% exclusion contour for the two-step gluino simplified model with sleptons from the hard single-lepton analysis.
Observed 95% exclusion contour for the two-step gluino simplified model with sleptons from the hard dilepton analysis.
Expected 95% exclusion contour for the two-step gluino simplified model with sleptons from the hard dilepton analysis.
Observed 95% exclusion contour for the two-step first- and second-generation squark simplified model with sleptons from the hard dilepton analysis.
Expected 95% exclusion contour for the two-step first- and second-generation squark simplified model with sleptons from the hard dilepton analysis.
Observed 95% exclusion contour for the two-step gluino simplified model without sleptons from the hard single-lepton analysis.
Expected 95% exclusion contour for the two-step gluino simplified model without sleptons from the hard single-lepton analysis.
Number of generated events in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Production cross-section in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Number of generated events in the the first- and second-generation squark simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV. squark decaying to quark neutralino1 with varying x.
Production cross-section in the the first- and second-generation squark simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Production cross-section in the minimal UED model in pb.
Number of generated events in the two-step first- and second-generation squark simplified model with sleptons.
Production cross-section in the two-step first- and second-generation squark simplified model with sleptons.
Acceptance for soft single-lepton 3-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Efficiency for soft single-lepton 3-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Acceptance for soft single-lepton 5-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Efficiency for soft single-lepton 5-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Acceptance for soft single-lepton 3-jet inclusive signal region in the gluino simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Efficiency for the soft single-lepton 3-jet inclusive signal region in the gluino simplified model for the case in x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Expected CLs from the combination of the soft single-lepton and hard single-lepton analyses in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Expected CLs from the combination of the soft single-lepton and hard single-lepton analyses in the gluino simplified model for the case in which the chargino mass is varied and the LSP mass is set at 60 GeV. The chargino mass is parameterised using x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)).
Observed CLs from the combination of the soft single-lepton and hard single-lepton analyses in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Observed CLs from the combination of the soft single-lepton and hard single-lepton analyses in the gluino simplified model for the case in which the chargino mass is varied and the LSP mass is set at 60 GeV. The chargino mass is parameterised using x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)).
Acceptance for hard dilepton 3-jet opposite-flavour signal region in the two-step first- and second-generation squark simplified model with sleptons.
Efficiency for hard dilepton 3jet opposite-flavour signal region in the two-step first- and second-generation squark simplified model with sleptons.
Acceptance for hard dilepton 3-jet same-flavour signal region in the two-step first- and second-generation squark simplified model with sleptons.
Efficiency for hard dilepton 3-jet same-flavour signal region in the two-step first- and second-generation squark simplified model with sleptons.
Acceptance for hard dilepton low-multiplicity opposite-flavour signal region in the two-step first- and second-generation squark simplified model with sleptons.
Efficiency for hard dilepton low-multiplicity opposite-flavour signal region in the two-step first- and second-generation squark simplified model with sleptons.
Acceptance for hard dilepton low-multiplicity same-flavour signal region in the two-step first- and second-generation squark simplified model with sleptons.
Efficiency for hard dilepton low-multiplicity same-flavour signal region in the two-step first- and second-generation squark simplified model with sleptons.
Expected CLs from hard dilepton analysis in the two-step first- and second-generation squark simplified model with sleptons.
Observed CLs from the hard dilepton analysis in the two-step first- and second-generation squark simplified model with sleptons.
Acceptance for hard single-lepton 3-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Efficiency for hard single-lepton 3-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Acceptance for hard single-lepton 5-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Efficiency for hard single-lepton 5-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Acceptance for hard single-lepton 6-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Efficiency for hard single-lepton 6-jet signal region in the gluino simplified model for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Acceptance for hard single-lepton 3-jet signal region in the first- and second-generation squark simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Efficiency for hard single-lepton 3-jet signal region in the first- and second-generation squark simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Acceptance for hard single-lepton 5-jet signal region in the first- and second-generation squark simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Efficiency for hard single-lepton 5-jet signal region in the first- and second-generation squark simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Acceptance for hard single-lepton 6-jet signal region in the first- and second-generation squark simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Efficiency for hard single-lepton 6-jet signal region in the first- and second-generation squark simplified model for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Observed 95% upper limit on the visible cross-section in the gluino simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which the chargino mass is fixed at x = (m(gluino)-m(chargino))/(m(gluino)-m(LSP)) = 1/2.
Observed 95% upper limit on the visible cross-section in the first- and second-generation squark simplified model from the combination of the soft single-lepton and hard single-lepton analyses for the case in which x = (m(squark)-m(chargino))/(m(squark)-m(LSP)) is varied and the LSP mass is set at 60 GeV.
Observed 95% upper limit on the visible cross-section in the first- and second-generation squark simplified model with sleptons from the hard dilepton analysis.
Observed 95% upper limit on the visible cross-section in the minimal UED model (mUED) from the combination of the soft dimuon and hard dilepton analyses.
The result of a search for flavor changing neutral currents (FCNC) through single top quark production in association with a photon is presented. The study is based on proton-proton collisions at a center-of-mass energy of 8 TeV using data collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 19.8 inverse femtobarns. The search for t gamma events where t to Wb and W to mu nu is conducted in final states with a muon, a photon, at least one hadronic jet with at most one being consistent with originating from a bottom quark, and missing transverse momentum. No evidence of single top quark production in association with a photon through a FCNC is observed. Upper limits at the 95% confidence level are set on the tu gamma and tc gamma anomalous couplings and translated into upper limits on the branching fraction of the FCNC top quark decays: B(t to u gamma) < 1.3E-4 and B(t to c gamma) < 1.7E-3. Upper limits are also set on the cross section of associated t gamma production in a restricted phase-space region. These are the most stringent limits currently available.
The expected and observed $95\%$ CL upper limits on the FCNC $tu\gamma$ and $tc\gamma$ cross sections times branching fraction, the anomalous couplings $\kappa_{tu\gamma}$ and $\kappa_{tc\gamma}$, and the corresponding branching fractions B($t \rightarrow u \gamma$)and B($t\rightarrow c \gamma$)at LO are given. The one and two standard deviation ($\sigma$) ranges on the LO expected limits are also presented.
The expected and observed $95\%$ CL upper limits on the FCNC $tu\gamma$ and $tc\gamma$ cross sections times branching fraction, the anomalous couplings $\kappa_{tu\gamma}$ and $\kappa_{tc\gamma}$, and the corresponding branching fractions B($t \rightarrow u \gamma$)and B($t\rightarrow c \gamma$)at NLO are given. The one and two standard deviation ($\sigma$) ranges on the NLO expected limits are also presented.
Upper limits on the signal cross sections are also determined for a restricted phase-space region in which the detector is fully efficient. This removes the need to extrapolate to phase-space regions where the analysis has little or no sensitivity. The fiducial region is defined as:.
For the tu$\gamma$ signal channel, the total number of observed selected events in the data (N$_{obs}$), the SM expectations (N$_{SM}$), the efficiencies ($\epsilon$), and the upper limits on the cross sections $\sigma_{fid}$ at the 95% CL in the fiducial region, without and with a requirement on the presence of a single accompanying b jet.
For the tc$\gamma$ signal channel, the total number of observed selected events in the data (N$_{obs}$), the SM expectations (N$_{SM}$), the efficiencies ($\epsilon$), and the upper limits on the cross sections $\sigma_{fid}$ at the 95% CL in the fiducial region, without and with a requirement on the presence of a single accompanying b jet.
The first collider search for dark matter arising from a strongly coupled hidden sector is presented and uses a data sample corresponding to 138 fb$^{-1}$, collected with the CMS detector at the CERN LHC, at $\sqrt{s} =$ 13 TeV. The hidden sector is hypothesized to couple to the standard model (SM) via a heavy leptophobic Z' mediator produced as a resonance in proton-proton collisions. The mediator decay results in two "semivisible" jets, containing both visible matter and invisible dark matter. The final state therefore includes moderate missing energy aligned with one of the jets, a signature ignored by most dark matter searches. No structure in the dijet transverse mass spectra compatible with the signal is observed. Assuming the Z' has a universal coupling of 0.25 to the SM quarks, an inclusive search, relevant to any model that exhibits this kinematic behavior, excludes mediator masses of 1.5-4.0 TeV at 95% confidence level, depending on the other signal model parameters. To enhance the sensitivity of the search for this particular class of hidden sector models, a boosted decision tree (BDT) is trained using jet substructure variables to distinguish between semivisible jets and SM jets from background processes. When the BDT is employed to identify each jet in the dijet system as semivisible, the mediator mass exclusion increases to 5.1 TeV, for wider ranges of the other signal model parameters. These limits exclude a wide range of strongly coupled hidden sector models for the first time.
The normalized distribution of the characteristic variable $R_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $R_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $R_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.
The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.
The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.
The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.
The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.
The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.
The three two-dimensional signal model parameter scans.
The three two-dimensional signal model parameter scans.
The three two-dimensional signal model parameter scans.
Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.
Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.
Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.
The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.
The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.
The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.
The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.
The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.
The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.
The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.
The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.
The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.
Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.
Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.
Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.
The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.
The proportions of each SM background process in the high-SVJ2 signal region.
The proportions of each SM background process in the high-SVJ2 signal region.
The proportions of each SM background process in the high-SVJ2 signal region.
The proportions of each SM background process in the low-SVJ2 signal region.
The proportions of each SM background process in the low-SVJ2 signal region.
The proportions of each SM background process in the low-SVJ2 signal region.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
The 95% CL expected upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.
Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.
The transverse polarization of $\Lambda$ and $\bar\Lambda$ hyperons produced in proton-proton collisions at a center-of-mass energy of 7 TeV is measured. The analysis uses 760 $\mu$b$^{-1}$ of minimum bias data collected by the ATLAS detector at the LHC in the year 2010. The measured transverse polarization averaged over Feynman $x_{\rm F}$ from $5\times 10^{-5}$ to 0.01 and transverse momentum $p_{\rm T}$ from 0.8 to 15 GeV is $-0.010 \pm 0.005({\rm stat}) \pm 0.004({\rm syst})$ for $\Lambda$ and $0.002 \pm 0.006({\rm stat}) \pm 0.004({\rm syst})$ for $\bar\Lambda$. It is also measured as a function of $x_{\rm F}$ and $p_{\rm T}$, but no significant dependence on these variables is observed. Prior to this measurement, the polarization was measured at fixed-target experiments with center-of-mass energies up to about 40 GeV. The ATLAS results are compatible with the extrapolation of a fit from previous measurements to the $x_{\rm F}$ range covered by this mesurement.
Transverse polarization POL of LAMBDA and LAMBDABAR hyperons averaged over PT and XF.
Transverse polarization POL of LAMBDA and LAMBDABAR hyperons as a function of XF.
Transverse polarization POL of LAMBDA and LAMBDABAR hyperons as a function of PT.
The LAMBDA reconstruction and selection efficiency EFF as a function of PT and XF.
The LAMBDABAR reconstruction and selection efficiency EFF as a function of PT and XF.
When you search on a word, e.g. 'collisions', we will automatically search across everything we store about a record. But, sometimes you may wish to be more specific. Here we show you how.
Guidance and examples on the query string syntax can be found in the Elasticsearch documentation.
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