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A search for decays of pair-produced neutral long-lived particles (LLPs) is presented using 139 fb$^{-1}$ of proton-proton collision data collected by the ATLAS detector at the LHC in 2015-2018 at a centre-of-mass energy of 13 TeV. Dedicated techniques were developed for the reconstruction of displaced jets produced by LLPs decaying hadronically in the ATLAS hadronic calorimeter. Two search regions are defined for different LLP kinematic regimes. The observed numbers of events are consistent with the expected background, and limits for several benchmark signals are determined. For a SM Higgs boson with a mass of 125 GeV, branching ratios above 10% are excluded at 95% confidence level for values of $c$ times LLP mean proper lifetime in the range between 20 mm and 10 m depending on the model. Upper limits are also set on the cross-section times branching ratio for scalars with a mass of 60 GeV and for masses between 200 GeV and 1 TeV.
CalRatio triggers which were available during the LHC Run 2 data-taking, and corresponding integrated luminosity collected in each period. The high-E<sub>T</sub> CalRatio trigger with E<sub>T</sub> > 60 GeV was disabled in 2017 for instantaneous luminosities higher than 1.4 × 10<sup>34</sup> cm<sup>-2</sup> s<sup>-1</sup>. Two versions of the low-E<sub>T</sub> CalRatio trigger were used, with slight differences in their algorithms. The details are reported in Section 4.
CalRatio triggers which were available during the LHC Run 2 data-taking, and corresponding integrated luminosity collected in each period. The high-E<sub>T</sub> CalRatio trigger with E<sub>T</sub> > 60 GeV was disabled in 2017 for instantaneous luminosities higher than 1.4 × 10<sup>34</sup> cm<sup>-2</sup> s<sup>-1</sup>. Two versions of the low-E<sub>T</sub> CalRatio trigger were used, with slight differences in their algorithms. The details are reported in Section 4.
CalRatio triggers which were available during the LHC Run 2 data-taking, and corresponding integrated luminosity collected in each period. The high-E<sub>T</sub> CalRatio trigger with E<sub>T</sub> > 60 GeV was disabled in 2017 for instantaneous luminosities higher than 1.4 × 10<sup>34</sup> cm<sup>-2</sup> s<sup>-1</sup>. Two versions of the low-E<sub>T</sub> CalRatio trigger were used, with slight differences in their algorithms. The details are reported in Section 4.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
Distribution of the low-E<sub>T</sub> per-event BDT in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Distribution of the low-E<sub>T</sub> per-event BDT in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Distribution of the low-E<sub>T</sub> per-event BDT in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Distribution of the high-E<sub>T</sub> per-event BDT outputs in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Distribution of the high-E<sub>T</sub> per-event BDT outputs in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Distribution of the high-E<sub>T</sub> per-event BDT outputs in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Sequential impact of each requirement on the number of events passing the selection for the high-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Sequential impact of each requirement on the number of events passing the selection for the high-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Sequential impact of each requirement on the number of events passing the selection for the high-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Sequential impact of each requirement on the number of events passing the selection for the low-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Sequential impact of each requirement on the number of events passing the selection for the low-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Sequential impact of each requirement on the number of events passing the selection for the low-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Application of the modified ABCD method to the final high-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
Application of the modified ABCD method to the final high-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
Application of the modified ABCD method to the final high-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
Application of the modified ABCD method to the final low-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
Application of the modified ABCD method to the final low-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
Application of the modified ABCD method to the final low-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
95% CL expected and observed limits on the BR of SM Higgs bosons to pairs of neutral LLPs (B<sub>H→ ss</sub>), showing the ± 1 σ (green) and ± 2 σ (yellow) expected limit bands, as well as a comparison with the results from previous ATLAS searches [36,78]. The cross-section for SM Higgs boson gluon--gluon fusion production is assumed to be 48.6 pb.
95% CL expected and observed limits on the BR of SM Higgs bosons to pairs of neutral LLPs (B<sub>H→ ss</sub>), showing the ± 1 σ (green) and ± 2 σ (yellow) expected limit bands, as well as a comparison with the results from previous ATLAS searches [36,78]. The cross-section for SM Higgs boson gluon--gluon fusion production is assumed to be 48.6 pb.
95% CL expected and observed limits on the BR of SM Higgs bosons to pairs of neutral LLPs (B<sub>H→ ss</sub>), showing the ± 1 σ (green) and ± 2 σ (yellow) expected limit bands, as well as a comparison with the results from previous ATLAS searches [36,78]. The cross-section for SM Higgs boson gluon--gluon fusion production is assumed to be 48.6 pb.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The event BDT in the dijet control region for the low-E<sub>T</sub> training training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The event BDT in the dijet control region for the low-E<sub>T</sub> training training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The event BDT in the dijet control region for the low-E<sub>T</sub> training training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The event BDT in the dijet control region for the high-E<sub>T</sub> training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The event BDT in the dijet control region for the high-E<sub>T</sub> training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The event BDT in the dijet control region for the high-E<sub>T</sub> training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 200 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 200 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 200 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 400 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 400 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 400 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
Efficiency for an event to enter Region A of the high-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in decay position in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of decay position bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the high-E<sub>T</sub> selections, for overall efficiencies above 0.5%, the results are typically accurate to around 25%, but below this the efficiency can be overestimated and therefore should not be used for re-interpration.
Efficiency for an event to enter Region A of the high-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in decay position in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of decay position bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the high-E<sub>T</sub> selections, for overall efficiencies above 0.5%, the results are typically accurate to around 25%, but below this the efficiency can be overestimated and therefore should not be used for re-interpration.
Efficiency for an event to enter Region A of the high-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in decay position in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of p<sub>T</sub> bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the high-E<sub>T</sub> selections, for overall efficiencies above 0.5%, the results are typically accurate to around 25%, but below this the efficiency can be overestimated and therefore should not be used for re-interpration.
Efficiency for an event to enter Region A of the low-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of decay position bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the low-E<sub>T</sub> selections, for overall efficiencies above 0.15%, the results are typically accurate to around 33%, and below this the efficiency is typically accurate up to a factor of 3.
Efficiency for an event to enter Region A of the low-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of decay position bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the low-E<sub>T</sub> selections, for overall efficiencies above 0.15%, the results are typically accurate to around 33%, and below this the efficiency is typically accurate up to a factor of 3.
Efficiency for an event to enter Region A of the low-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of p<sub>T</sub> bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the low-E<sub>T</sub> selections, for overall efficiencies above 0.15%, the results are typically accurate to around 33%, and below this the efficiency is typically accurate up to a factor of 3.
Searches are performed for nonresonant and resonant di-Higgs boson production in the $b\bar{b}\gamma\gamma$ final state. The data set used corresponds to an integrated luminosity of 139 fb$^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. No excess above the expected background is found and upper limits on the di-Higgs boson production cross sections are set. A 95% confidence-level upper limit of 4.2 times the cross section predicted by the Standard Model is set on $pp \rightarrow HH$ nonresonant production, where the expected limit is 5.7 times the Standard Model predicted value. The expected constraints are obtained for a background hypothesis excluding $pp \rightarrow HH$ production. The observed (expected) constraints on the Higgs boson trilinear coupling modifier $\kappa_{\lambda}$ are determined to be $[-1.5, 6.7]$ $([-2.4, 7.7])$ at 95% confidence level, where the expected constraints on $\kappa_{\lambda}$ are obtained excluding $pp \rightarrow HH$ production from the background hypothesis. For resonant production of a new hypothetical scalar particle $X$ ($X \rightarrow HH \rightarrow b\bar{b}\gamma\gamma$), limits on the cross section for $pp \to X \to HH$ are presented in the narrow-width approximation as a function of $m_{X}$ in the range $251 \leq m_{X} \leq 1000$ GeV. The observed (expected) limits on the cross section for $pp \to X \to HH$ range from 640 fb to 44 fb (391 fb to 46 fb) over the considered mass range.
The BDT distribution of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the low mass region ($m^{*}_{b\bar{b}\gamma\gamma} < 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.881 in the low mass region are discarded.
The BDT distribution of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the low mass region ($m^{*}_{b\bar{b}\gamma\gamma} < 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.881 in the low mass region are discarded.
The BDT distribution of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the low mass region ($m^{*}_{b\bar{b}\gamma\gamma} < 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.881 in the low mass region are discarded.
The BDT distribution of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the low mass region ($m^{*}_{b\bar{b}\gamma\gamma} < 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.881 in the low mass region are discarded.
The BDT distribution (with x-axis zoomed in) of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the low mass region ($m^{*}_{b\bar{b}\gamma\gamma} < 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.881 in the low mass region are discarded. The range of BDT scores is from 0.8 to 1.
The BDT distribution (with x-axis zoomed in) of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the low mass region ($m^{*}_{b\bar{b}\gamma\gamma} < 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.881 in the low mass region are discarded. The range of BDT scores is from 0.8 to 1.
The BDT distribution (with x-axis zoomed in) of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the low mass region ($m^{*}_{b\bar{b}\gamma\gamma} < 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.881 in the low mass region are discarded. The range of BDT scores is from 0.8 to 1.
The BDT distribution (with x-axis zoomed in) of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the low mass region ($m^{*}_{b\bar{b}\gamma\gamma} < 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.881 in the low mass region are discarded. The range of BDT scores is from 0.8 to 1.
The BDT distribution of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the high mass region ($m^{*}_{b\bar{b}\gamma\gamma} > 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.857 in the high mass region are discarded.
The BDT distribution of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the high mass region ($m^{*}_{b\bar{b}\gamma\gamma} > 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.857 in the high mass region are discarded.
The BDT distribution of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the high mass region ($m^{*}_{b\bar{b}\gamma\gamma} > 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.857 in the high mass region are discarded.
The BDT distribution of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the high mass region ($m^{*}_{b\bar{b}\gamma\gamma} > 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.857 in the high mass region are discarded.
The BDT distribution (with x-axis zoomed in) of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the high mass region ($m^{*}_{b\bar{b}\gamma\gamma} > 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.857 in the high mass region are discarded. The range of BDT scores is from 0.8 to 1.
The BDT distribution (with x-axis zoomed in) of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the high mass region ($m^{*}_{b\bar{b}\gamma\gamma} > 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.857 in the high mass region are discarded. The range of BDT scores is from 0.8 to 1.
The BDT distribution (with x-axis zoomed in) of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the high mass region ($m^{*}_{b\bar{b}\gamma\gamma} > 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.857 in the high mass region are discarded. The range of BDT scores is from 0.8 to 1.
The BDT distribution (with x-axis zoomed in) of the di-Higgs ggF signal for two different values of $\kappa_{\lambda}$ and the main backgrounds in the high mass region ($m^{*}_{b\bar{b}\gamma\gamma} > 350$ GeV). Distributions are normalized to unit area. The dotted lines denote the category boundaries. Events with a BDT score below 0.857 in the high mass region are discarded. The range of BDT scores is from 0.8 to 1.
The $BDT_{tot}$ score for the benchmark signal $m_{X}$ = 300 GeV and for the main backgrounds. Distributions are normalized to unit area. The dotted line denotes the event selection threshold. Events with a $BDT_{tot}$ score below 0.85 for $m_{X}$ = 300 GeV are discarded.
The $BDT_{tot}$ score for the benchmark signal $m_{X}$ = 300 GeV and for the main backgrounds. Distributions are normalized to unit area. The dotted line denotes the event selection threshold. Events with a $BDT_{tot}$ score below 0.85 for $m_{X}$ = 300 GeV are discarded.
The $BDT_{tot}$ score for the benchmark signal $m_{X}$ = 300 GeV and for the main backgrounds. Distributions are normalized to unit area. The dotted line denotes the event selection threshold. Events with a $BDT_{tot}$ score below 0.85 for $m_{X}$ = 300 GeV are discarded.
The $BDT_{tot}$ score for the benchmark signal $m_{X}$ = 300 GeV and for the main backgrounds. Distributions are normalized to unit area. The dotted line denotes the event selection threshold. Events with a $BDT_{tot}$ score below 0.85 for $m_{X}$ = 300 GeV are discarded.
The $BDT_{tot}$ score for the benchmark signal $m_{X}$ = 500 GeV and for the main backgrounds. Distributions are normalized to unit area. The dotted line denotes the event selection threshold. Events with a $BDT_{tot}$ score below 0.75 for $m_{X}$ = 500 GeV are discarded.
The $BDT_{tot}$ score for the benchmark signal $m_{X}$ = 500 GeV and for the main backgrounds. Distributions are normalized to unit area. The dotted line denotes the event selection threshold. Events with a $BDT_{tot}$ score below 0.75 for $m_{X}$ = 500 GeV are discarded.
The $BDT_{tot}$ score for the benchmark signal $m_{X}$ = 500 GeV and for the main backgrounds. Distributions are normalized to unit area. The dotted line denotes the event selection threshold. Events with a $BDT_{tot}$ score below 0.75 for $m_{X}$ = 500 GeV are discarded.
The $BDT_{tot}$ score for the benchmark signal $m_{X}$ = 500 GeV and for the main backgrounds. Distributions are normalized to unit area. The dotted line denotes the event selection threshold. Events with a $BDT_{tot}$ score below 0.75 for $m_{X}$ = 500 GeV are discarded.
Distributions of $m_{\gamma\gamma}$ in high mass BDT tight category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in high mass BDT tight category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in high mass BDT tight category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in high mass BDT tight category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in high mass BDT loose category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in high mass BDT loose category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in high mass BDT loose category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in high mass BDT loose category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in low mass BDT tight category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in low mass BDT tight category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in low mass BDT tight category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in low mass BDT tight category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in low mass BDT loose category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in low mass BDT loose category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in low mass BDT loose category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ in low mass BDT loose category for the nonresonant $HH$ search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband.
Distributions of $m_{\gamma\gamma}$ for the selections used for the resonance mass point $m_{X}$ = 300 GeV for the resonant search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband. The scalar resonance signal is scaled to a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ = 370 fb.
Distributions of $m_{\gamma\gamma}$ for the selections used for the resonance mass point $m_{X}$ = 300 GeV for the resonant search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband. The scalar resonance signal is scaled to a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ = 370 fb.
Distributions of $m_{\gamma\gamma}$ for the selections used for the resonance mass point $m_{X}$ = 300 GeV for the resonant search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband. The scalar resonance signal is scaled to a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ = 370 fb.
Distributions of $m_{\gamma\gamma}$ for the selections used for the resonance mass point $m_{X}$ = 300 GeV for the resonant search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband. The scalar resonance signal is scaled to a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ = 370 fb.
Distributions of $m_{\gamma\gamma}$ for the selections used for the resonance mass point $m_{X}$ = 500 GeV for the resonant search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband. The scalar resonance signal is scaled to a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ = 67 fb.
Distributions of $m_{\gamma\gamma}$ for the selections used for the resonance mass point $m_{X}$ = 500 GeV for the resonant search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband. The scalar resonance signal is scaled to a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ = 67 fb.
Distributions of $m_{\gamma\gamma}$ for the selections used for the resonance mass point $m_{X}$ = 500 GeV for the resonant search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband. The scalar resonance signal is scaled to a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ = 67 fb.
Distributions of $m_{\gamma\gamma}$ for the selections used for the resonance mass point $m_{X}$ = 500 GeV for the resonant search. The data-derived fractions of nonresonant $\gamma\gamma$, $\gamma$-jet or jet-$\gamma$, and dijet background are applied and the total background is normalized to the data sideband. The scalar resonance signal is scaled to a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ = 67 fb.
The number of data events observed in the 120 GeV < $m_{\gamma\gamma}$ < 130 GeV window, the number of $HH$ signal events expected for $\kappa_{\lambda}$ = 1 and for $\kappa_{\lambda}$ = 10, and events expected for single Higgs boson production (estimated using MC simulation), as well as for continuum background. For the single Higgs boson, Rest includes VBF, $WH$, $tHqb$, and $tHW$. The values are obtained from a fit of the Asimov data set generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$ = 1. The continuum background component of the Asimov data set is obtained from the fit of the data sideband. The uncertainties in $HH$ signals and single Higgs boson background include the systematic uncertainties discussed in Section 6. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious-signal uncertainty.
The number of data events observed in the 120 GeV < $m_{\gamma\gamma}$ < 130 GeV window, the number of $HH$ signal events expected for $\kappa_{\lambda}$ = 1 and for $\kappa_{\lambda}$ = 10, and events expected for single Higgs boson production (estimated using MC simulation), as well as for continuum background. For the single Higgs boson, Rest includes VBF, $WH$, $tHqb$, and $tHW$. The values are obtained from a fit of the Asimov data set generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$ = 1. The continuum background component of the Asimov data set is obtained from the fit of the data sideband. The uncertainties in $HH$ signals and single Higgs boson background include the systematic uncertainties discussed in Section 6. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious-signal uncertainty.
The number of data events observed in the 120 GeV < $m_{\gamma\gamma}$ < 130 GeV window, the number of $HH$ signal events expected for $\kappa_{\lambda}$ = 1 and for $\kappa_{\lambda}$ = 10, and events expected for single Higgs boson production (estimated using MC simulation), as well as for continuum background. For the single Higgs boson, Rest includes VBF, $WH$, $tHqb$, and $tHW$. The values are obtained from a fit of the Asimov data set generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$ = 1. The continuum background component of the Asimov data set is obtained from the fit of the data sideband. The uncertainties in $HH$ signals and single Higgs boson background include the systematic uncertainties discussed in Section 6. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious-signal uncertainty.
The number of data events observed in the 120 GeV < $m_{\gamma\gamma}$ < 130 GeV window, the number of $HH$ signal events expected for $\kappa_{\lambda}$ = 1 and for $\kappa_{\lambda}$ = 10, and events expected for single Higgs boson production (estimated using MC simulation), as well as for continuum background. For the single Higgs boson, Rest includes VBF, $WH$, $tHqb$, and $tHW$. The values are obtained from a fit of the Asimov data set generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$ = 1. The continuum background component of the Asimov data set is obtained from the fit of the data sideband. The uncertainties in $HH$ signals and single Higgs boson background include the systematic uncertainties discussed in Section 6. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious-signal uncertainty.
Observed and expected limits at 95% CL on the cross section of nonresonant Higgs boson pair production as a function of the Higgs boson self-coupling modifier $\kappa_{\lambda}= \lambda_{HHH}/\lambda^{\textrm{SM}}_{HHH}$. The expected constraints on $\kappa_{\lambda}$ are obtained with a background hypothesis excluding $pp \rightarrow HH$ production. The $\pm 1\sigma$ and $\pm 2\sigma$ variations about the expected limit due to statistical and systematic uncertainties are also shown. The theory prediction curve represents the scenario where all parameters and couplings are set to their SM values except for $\kappa_{\lambda}$. The uncertainty band of the theory prediction curve shows the cross-section uncertainty.
Observed and expected limits at 95% CL on the cross section of nonresonant Higgs boson pair production as a function of the Higgs boson self-coupling modifier $\kappa_{\lambda}= \lambda_{HHH}/\lambda^{\textrm{SM}}_{HHH}$. The expected constraints on $\kappa_{\lambda}$ are obtained with a background hypothesis excluding $pp \rightarrow HH$ production. The $\pm 1\sigma$ and $\pm 2\sigma$ variations about the expected limit due to statistical and systematic uncertainties are also shown. The theory prediction curve represents the scenario where all parameters and couplings are set to their SM values except for $\kappa_{\lambda}$. The uncertainty band of the theory prediction curve shows the cross-section uncertainty.
Observed and expected limits at 95% CL on the cross section of nonresonant Higgs boson pair production as a function of the Higgs boson self-coupling modifier $\kappa_{\lambda}= \lambda_{HHH}/\lambda^{\textrm{SM}}_{HHH}$. The expected constraints on $\kappa_{\lambda}$ are obtained with a background hypothesis excluding $pp \rightarrow HH$ production. The $\pm 1\sigma$ and $\pm 2\sigma$ variations about the expected limit due to statistical and systematic uncertainties are also shown. The theory prediction curve represents the scenario where all parameters and couplings are set to their SM values except for $\kappa_{\lambda}$. The uncertainty band of the theory prediction curve shows the cross-section uncertainty.
Observed and expected limits at 95% CL on the cross section of nonresonant Higgs boson pair production as a function of the Higgs boson self-coupling modifier $\kappa_{\lambda}= \lambda_{HHH}/\lambda^{\textrm{SM}}_{HHH}$. The expected constraints on $\kappa_{\lambda}$ are obtained with a background hypothesis excluding $pp \rightarrow HH$ production. The $\pm 1\sigma$ and $\pm 2\sigma$ variations about the expected limit due to statistical and systematic uncertainties are also shown. The theory prediction curve represents the scenario where all parameters and couplings are set to their SM values except for $\kappa_{\lambda}$. The uncertainty band of the theory prediction curve shows the cross-section uncertainty.
Values of the negative log-profile-likelihood ratio ($-2ln\Lambda$) as a function of $\kappa_{\lambda}$ evaluated for the combination of all the categories of the nonresonant search. The coupling of the Higgs boson to fermions and gauge bosons is set to SM values in the profile likelihood calculation. The Asimov data set is generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$= 1. All systematic uncertainties, including the theoretical uncertainties in the di-Higgs boson production cross section, are included. The intersections of the solid curves and the horizontal dashed lines indicate the 1$\sigma$ and 2$\sigma$ confidence-level intervals.
Values of the negative log-profile-likelihood ratio ($-2ln\Lambda$) as a function of $\kappa_{\lambda}$ evaluated for the combination of all the categories of the nonresonant search. The coupling of the Higgs boson to fermions and gauge bosons is set to SM values in the profile likelihood calculation. The Asimov data set is generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$= 1. All systematic uncertainties, including the theoretical uncertainties in the di-Higgs boson production cross section, are included. The intersections of the solid curves and the horizontal dashed lines indicate the 1$\sigma$ and 2$\sigma$ confidence-level intervals.
Values of the negative log-profile-likelihood ratio ($-2ln\Lambda$) as a function of $\kappa_{\lambda}$ evaluated for the combination of all the categories of the nonresonant search. The coupling of the Higgs boson to fermions and gauge bosons is set to SM values in the profile likelihood calculation. The Asimov data set is generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$= 1. All systematic uncertainties, including the theoretical uncertainties in the di-Higgs boson production cross section, are included. The intersections of the solid curves and the horizontal dashed lines indicate the 1$\sigma$ and 2$\sigma$ confidence-level intervals.
Values of the negative log-profile-likelihood ratio ($-2ln\Lambda$) as a function of $\kappa_{\lambda}$ evaluated for the combination of all the categories of the nonresonant search. The coupling of the Higgs boson to fermions and gauge bosons is set to SM values in the profile likelihood calculation. The Asimov data set is generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$= 1. All systematic uncertainties, including the theoretical uncertainties in the di-Higgs boson production cross section, are included. The intersections of the solid curves and the horizontal dashed lines indicate the 1$\sigma$ and 2$\sigma$ confidence-level intervals.
The number of events observed in the 120 < $m_{\gamma\gamma}$ < 130 GeV window in data, the number of events expected for scalar resonance signals of masses $m_{X}$ = 300 GeV and $m_{X}$ = 500 GeV assuming a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ equal to the observed exclusion limits of Figure 15, and events expected for SM $HH$ and single Higgs boson production (estimated using MC simulation), as well as for continuum background. The values are obtained from a fit of the Asimov data set generated under the signal-plus-background hypothesis. The continuum background component of the Asimov data set is obtained from the fit of the data sideband. The uncertainties in the resonant signals and the SM $HH$ and single-Higgs-boson backgrounds include the systematic uncertainties discussed in Section 6. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious-signal uncertainty.
The number of events observed in the 120 < $m_{\gamma\gamma}$ < 130 GeV window in data, the number of events expected for scalar resonance signals of masses $m_{X}$ = 300 GeV and $m_{X}$ = 500 GeV assuming a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ equal to the observed exclusion limits of Figure 15, and events expected for SM $HH$ and single Higgs boson production (estimated using MC simulation), as well as for continuum background. The values are obtained from a fit of the Asimov data set generated under the signal-plus-background hypothesis. The continuum background component of the Asimov data set is obtained from the fit of the data sideband. The uncertainties in the resonant signals and the SM $HH$ and single-Higgs-boson backgrounds include the systematic uncertainties discussed in Section 6. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious-signal uncertainty.
The number of events observed in the 120 < $m_{\gamma\gamma}$ < 130 GeV window in data, the number of events expected for scalar resonance signals of masses $m_{X}$ = 300 GeV and $m_{X}$ = 500 GeV assuming a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ equal to the observed exclusion limits of Figure 15, and events expected for SM $HH$ and single Higgs boson production (estimated using MC simulation), as well as for continuum background. The values are obtained from a fit of the Asimov data set generated under the signal-plus-background hypothesis. The continuum background component of the Asimov data set is obtained from the fit of the data sideband. The uncertainties in the resonant signals and the SM $HH$ and single-Higgs-boson backgrounds include the systematic uncertainties discussed in Section 6. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious-signal uncertainty.
The number of events observed in the 120 < $m_{\gamma\gamma}$ < 130 GeV window in data, the number of events expected for scalar resonance signals of masses $m_{X}$ = 300 GeV and $m_{X}$ = 500 GeV assuming a total production cross section $\sigma(pp \rightarrow X \rightarrow HH)$ equal to the observed exclusion limits of Figure 15, and events expected for SM $HH$ and single Higgs boson production (estimated using MC simulation), as well as for continuum background. The values are obtained from a fit of the Asimov data set generated under the signal-plus-background hypothesis. The continuum background component of the Asimov data set is obtained from the fit of the data sideband. The uncertainties in the resonant signals and the SM $HH$ and single-Higgs-boson backgrounds include the systematic uncertainties discussed in Section 6. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious-signal uncertainty.
Observed and expected limits at 95% CL on the production cross section of a narrow-width scalar resonance $X$ as a function of the mass $m_{X}$ of the hypothetical scalar particle. The black solid line represents the observed upper limits. The dashed line represents the expected upper limits. The $\pm 1\sigma$ and $\pm 2\sigma$ variations about the expected limit due to statistical and systematic uncertainties are also shown.
Observed and expected limits at 95% CL on the production cross section of a narrow-width scalar resonance $X$ as a function of the mass $m_{X}$ of the hypothetical scalar particle. The black solid line represents the observed upper limits. The dashed line represents the expected upper limits. The $\pm 1\sigma$ and $\pm 2\sigma$ variations about the expected limit due to statistical and systematic uncertainties are also shown.
Observed and expected limits at 95% CL on the production cross section of a narrow-width scalar resonance $X$ as a function of the mass $m_{X}$ of the hypothetical scalar particle. The black solid line represents the observed upper limits. The dashed line represents the expected upper limits. The $\pm 1\sigma$ and $\pm 2\sigma$ variations about the expected limit due to statistical and systematic uncertainties are also shown.
Observed and expected limits at 95% CL on the production cross section of a narrow-width scalar resonance $X$ as a function of the mass $m_{X}$ of the hypothetical scalar particle. The black solid line represents the observed upper limits. The dashed line represents the expected upper limits. The $\pm 1\sigma$ and $\pm 2\sigma$ variations about the expected limit due to statistical and systematic uncertainties are also shown.
Breakdown of the dominant systematic uncertainties. The impact of the uncertainties is defined according to the statistical analysis described in Section 7. It corresponds to the relative variation of the expected upper limit on the cross section when re-evaluating the profile likelihood ratio after fixing the nuisance parameter in question to its best-fit value, while all remaining nuisance parameters remain free to float. The impact is shown in %. Only systematic uncertainties with an impact of at least 0.2% are shown. Uncertainties of the "Norm. + Shape" type affect both the normalization and the parameters of the functional form. The rest of the uncertainties affect only the yields.
Breakdown of the dominant systematic uncertainties. The impact of the uncertainties is defined according to the statistical analysis described in Section 7. It corresponds to the relative variation of the expected upper limit on the cross section when re-evaluating the profile likelihood ratio after fixing the nuisance parameter in question to its best-fit value, while all remaining nuisance parameters remain free to float. The impact is shown in %. Only systematic uncertainties with an impact of at least 0.2% are shown. Uncertainties of the "Norm. + Shape" type affect both the normalization and the parameters of the functional form. The rest of the uncertainties affect only the yields.
Breakdown of the dominant systematic uncertainties. The impact of the uncertainties is defined according to the statistical analysis described in Section 7. It corresponds to the relative variation of the expected upper limit on the cross section when re-evaluating the profile likelihood ratio after fixing the nuisance parameter in question to its best-fit value, while all remaining nuisance parameters remain free to float. The impact is shown in %. Only systematic uncertainties with an impact of at least 0.2% are shown. Uncertainties of the "Norm. + Shape" type affect both the normalization and the parameters of the functional form. The rest of the uncertainties affect only the yields.
Breakdown of the dominant systematic uncertainties. The impact of the uncertainties is defined according to the statistical analysis described in Section 7. It corresponds to the relative variation of the expected upper limit on the cross section when re-evaluating the profile likelihood ratio after fixing the nuisance parameter in question to its best-fit value, while all remaining nuisance parameters remain free to float. The impact is shown in %. Only systematic uncertainties with an impact of at least 0.2% are shown. Uncertainties of the "Norm. + Shape" type affect both the normalization and the parameters of the functional form. The rest of the uncertainties affect only the yields.
Cutflow for nonresonant di-Higgs ggF signal sample, yields are normalized to 139 $fb^{-1}$.
Cutflow for nonresonant di-Higgs ggF signal sample, yields are normalized to 139 $fb^{-1}$.
Cutflow for nonresonant di-Higgs ggF signal sample, yields are normalized to 139 $fb^{-1}$.
Cutflow for nonresonant di-Higgs ggF signal sample, yields are normalized to 139 $fb^{-1}$.
Cutflow for resonant signal sample, with $m_{X}$ = 300 GeV, yields are normalized to 139 $fb^{-1}$.
Cutflow for resonant signal sample, with $m_{X}$ = 300 GeV, yields are normalized to 139 $fb^{-1}$.
Cutflow for resonant signal sample, with $m_{X}$ = 300 GeV, yields are normalized to 139 $fb^{-1}$.
Cutflow for resonant signal sample, with $m_{X}$ = 300 GeV, yields are normalized to 139 $fb^{-1}$.
Cutflow for resonant signal sample, with $m_{X}$ = 500 GeV, yields are normalized to 139 $fb^{-1}$.
Cutflow for resonant signal sample, with $m_{X}$ = 500 GeV, yields are normalized to 139 $fb^{-1}$.
Cutflow for resonant signal sample, with $m_{X}$ = 500 GeV, yields are normalized to 139 $fb^{-1}$.
Cutflow for resonant signal sample, with $m_{X}$ = 500 GeV, yields are normalized to 139 $fb^{-1}$.
Comparison of $m_{b\bar{b}}$ distributions when applying the specific b-jet energy calibration and the nominal jet energy calibration. The distributions are fitted using a Bukin function, and the values of the peak position, resolution and the relative improvement are reported in the legend.
Comparison of $m_{b\bar{b}}$ distributions when applying the specific b-jet energy calibration and the nominal jet energy calibration. The distributions are fitted using a Bukin function, and the values of the peak position, resolution and the relative improvement are reported in the legend.
Comparison of $m_{b\bar{b}}$ distributions when applying the specific b-jet energy calibration and the nominal jet energy calibration. The distributions are fitted using a Bukin function, and the values of the peak position, resolution and the relative improvement are reported in the legend.
Comparison of $m_{b\bar{b}}$ distributions when applying the specific b-jet energy calibration and the nominal jet energy calibration. The distributions are fitted using a Bukin function, and the values of the peak position, resolution and the relative improvement are reported in the legend.
Fit results of $m_{b\bar{b}}$ distributions when applying the specific b-jet energy calibration and the nominal jet energy calibration. The distributions are fitted using a Bukin function, and the values of the peak position, resolution and the relative improvement are reported in the legend.
Fit results of $m_{b\bar{b}}$ distributions when applying the specific b-jet energy calibration and the nominal jet energy calibration. The distributions are fitted using a Bukin function, and the values of the peak position, resolution and the relative improvement are reported in the legend.
Fit results of $m_{b\bar{b}}$ distributions when applying the specific b-jet energy calibration and the nominal jet energy calibration. The distributions are fitted using a Bukin function, and the values of the peak position, resolution and the relative improvement are reported in the legend.
Fit results of $m_{b\bar{b}}$ distributions when applying the specific b-jet energy calibration and the nominal jet energy calibration. The distributions are fitted using a Bukin function, and the values of the peak position, resolution and the relative improvement are reported in the legend.
The relative amount (purity) of expected events from SM $HH$ and single Higgs boson production processes for each of the four categories of the nonresonant search. The Higgs boson pair production with $k_{\lambda} = 1$ is considered as signal in (a), while the case with $k_{\lambda} = 10$ is considered as signal in (b).
The relative amount (purity) of expected events from SM $HH$ and single Higgs boson production processes for each of the four categories of the nonresonant search. The Higgs boson pair production with $k_{\lambda} = 1$ is considered as signal in (a), while the case with $k_{\lambda} = 10$ is considered as signal in (b).
The relative amount (purity) of expected events from SM $HH$ and single Higgs boson production processes for each of the four categories of the nonresonant search. The Higgs boson pair production with $k_{\lambda} = 1$ is considered as signal in (a), while the case with $k_{\lambda} = 10$ is considered as signal in (b).
The relative amount (purity) of expected events from SM $HH$ and single Higgs boson production processes for each of the four categories of the nonresonant search. The Higgs boson pair production with $k_{\lambda} = 1$ is considered as signal in (a), while the case with $k_{\lambda} = 10$ is considered as signal in (b).
The expected significance in each of the four categories of the nonresonant search. The Higgs boson pair production with $k_{\lambda} = 1$ is considered as signal in (a), while the case with $k_{\lambda} = 10$ is considered as signal in (b). The single Higgs boson processes and the di-photon continuum spectrum are considered as background.
The expected significance in each of the four categories of the nonresonant search. The Higgs boson pair production with $k_{\lambda} = 1$ is considered as signal in (a), while the case with $k_{\lambda} = 10$ is considered as signal in (b). The single Higgs boson processes and the di-photon continuum spectrum are considered as background.
The expected significance in each of the four categories of the nonresonant search. The Higgs boson pair production with $k_{\lambda} = 1$ is considered as signal in (a), while the case with $k_{\lambda} = 10$ is considered as signal in (b). The single Higgs boson processes and the di-photon continuum spectrum are considered as background.
The expected significance in each of the four categories of the nonresonant search. The Higgs boson pair production with $k_{\lambda} = 1$ is considered as signal in (a), while the case with $k_{\lambda} = 10$ is considered as signal in (b). The single Higgs boson processes and the di-photon continuum spectrum are considered as background.
Spurious signal result for the exponential pdf for the various ggF nonresonant di-Higgs categories. In each category, the spurious signal value ($n_{sp}$) and its ratio to the expected statistical error ($Z_{spur}$) from data are shown.
Spurious signal result for the exponential pdf for the various ggF nonresonant di-Higgs categories. In each category, the spurious signal value ($n_{sp}$) and its ratio to the expected statistical error ($Z_{spur}$) from data are shown.
Spurious signal result for the exponential pdf for the various ggF nonresonant di-Higgs categories. In each category, the spurious signal value ($n_{sp}$) and its ratio to the expected statistical error ($Z_{spur}$) from data are shown.
Spurious signal result for the exponential pdf for the various ggF nonresonant di-Higgs categories. In each category, the spurious signal value ($n_{sp}$) and its ratio to the expected statistical error ($Z_{spur}$) from data are shown.
Spurious signal result for the exponential pdf as function of the resonant di-Higgs signal mass. The spurious signal value and its ratio to the expected statistical error from data are shown.
Spurious signal result for the exponential pdf as function of the resonant di-Higgs signal mass. The spurious signal value and its ratio to the expected statistical error from data are shown.
Spurious signal result for the exponential pdf as function of the resonant di-Higgs signal mass. The spurious signal value and its ratio to the expected statistical error from data are shown.
Spurious signal result for the exponential pdf as function of the resonant di-Higgs signal mass. The spurious signal value and its ratio to the expected statistical error from data are shown.
Distributions of the signal efficiency as a function of $\kappa_{\lambda}$, for the di-Higgs boson ggF nonresonant production mode. The range of $\kappa_{\lambda}$ in the table is from -10 to 10.
Distributions of the signal efficiency as a function of $\kappa_{\lambda}$, for the di-Higgs boson ggF nonresonant production mode. The range of $\kappa_{\lambda}$ in the table is from -10 to 10.
Distributions of the signal efficiency as a function of $\kappa_{\lambda}$, for the di-Higgs boson ggF nonresonant production mode. The range of $\kappa_{\lambda}$ in the table is from -10 to 10.
Distributions of the signal efficiency as a function of $\kappa_{\lambda}$, for the di-Higgs boson ggF nonresonant production mode. The range of $\kappa_{\lambda}$ in the table is from -10 to 10.
Distributions of the signal efficiency as a function of $\kappa_{\lambda}$, for the di-Higgs boson VBF nonresonant production mode. The range of $\kappa_{\lambda}$ in the table is from -10 to 10.
Distributions of the signal efficiency as a function of $\kappa_{\lambda}$, for the di-Higgs boson VBF nonresonant production mode. The range of $\kappa_{\lambda}$ in the table is from -10 to 10.
Distributions of the signal efficiency as a function of $\kappa_{\lambda}$, for the di-Higgs boson VBF nonresonant production mode. The range of $\kappa_{\lambda}$ in the table is from -10 to 10.
Distributions of the signal efficiency as a function of $\kappa_{\lambda}$, for the di-Higgs boson VBF nonresonant production mode. The range of $\kappa_{\lambda}$ in the table is from -10 to 10.
Values of the negative log-profile-likelihood ($-2ln\Lambda$) as a function of $\kappa_{\lambda}$ evaluated for the combination of all the categories of the nonresonant search. The coupling of the Higgs boson to fermions and gauge bosons is set to SM values in the profile likelihood calculation. The Asimov data set is generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$= 1. All systematic uncertainties, including the theoretical uncertainties on the di-Higgs boson production cross section, are included. The intersections of the solid curves and the horizontal dashed lines indicate the 1$\sigma$ and 2$\sigma$ confidence level intervals. The best fit value corresponds to $\kappa_{\lambda}$ = $2.8^{+2.0}_{-2.2}(^{+3.8}_{-4.3})$ for the 1$\sigma$(2$\sigma$) confidence interval. The expected value corresponds to $\kappa_{\lambda}$ = $1.0^{+5.5}_{-2.4}(^{+7.3}_{-4.2})$ for the 1$\sigma$(2$\sigma$) confidence interval. The dashed curves represent values of the negative log-profile-likelihood where the Higgs boson branching fractions and the cross section of the production modes are varied as a function of $\kappa_{\lambda}$. In this case,the best fit value corresponds to $\kappa_{\lambda}$ = $2.7^{+2.0}_{-2.2}(^{+3.8}_{-4.3})$ and the expected value corresponds to $\kappa_{\lambda}$ = $1.0^{+5.4}_{-2.5}(^{+7.3}_{-4.3})$ for the 1$\sigma$(2$\sigma$) confidence interval.
Values of the negative log-profile-likelihood ($-2ln\Lambda$) as a function of $\kappa_{\lambda}$ evaluated for the combination of all the categories of the nonresonant search. The coupling of the Higgs boson to fermions and gauge bosons is set to SM values in the profile likelihood calculation. The Asimov data set is generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$= 1. All systematic uncertainties, including the theoretical uncertainties on the di-Higgs boson production cross section, are included. The intersections of the solid curves and the horizontal dashed lines indicate the 1$\sigma$ and 2$\sigma$ confidence level intervals. The best fit value corresponds to $\kappa_{\lambda}$ = $2.8^{+2.0}_{-2.2}(^{+3.8}_{-4.3})$ for the 1$\sigma$(2$\sigma$) confidence interval. The expected value corresponds to $\kappa_{\lambda}$ = $1.0^{+5.5}_{-2.4}(^{+7.3}_{-4.2})$ for the 1$\sigma$(2$\sigma$) confidence interval. The dashed curves represent values of the negative log-profile-likelihood where the Higgs boson branching fractions and the cross section of the production modes are varied as a function of $\kappa_{\lambda}$. In this case,the best fit value corresponds to $\kappa_{\lambda}$ = $2.7^{+2.0}_{-2.2}(^{+3.8}_{-4.3})$ and the expected value corresponds to $\kappa_{\lambda}$ = $1.0^{+5.4}_{-2.5}(^{+7.3}_{-4.3})$ for the 1$\sigma$(2$\sigma$) confidence interval.
Values of the negative log-profile-likelihood ($-2ln\Lambda$) as a function of $\kappa_{\lambda}$ evaluated for the combination of all the categories of the nonresonant search. The coupling of the Higgs boson to fermions and gauge bosons is set to SM values in the profile likelihood calculation. The Asimov data set is generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$= 1. All systematic uncertainties, including the theoretical uncertainties on the di-Higgs boson production cross section, are included. The intersections of the solid curves and the horizontal dashed lines indicate the 1$\sigma$ and 2$\sigma$ confidence level intervals. The best fit value corresponds to $\kappa_{\lambda}$ = $2.8^{+2.0}_{-2.2}(^{+3.8}_{-4.3})$ for the 1$\sigma$(2$\sigma$) confidence interval. The expected value corresponds to $\kappa_{\lambda}$ = $1.0^{+5.5}_{-2.4}(^{+7.3}_{-4.2})$ for the 1$\sigma$(2$\sigma$) confidence interval. The dashed curves represent values of the negative log-profile-likelihood where the Higgs boson branching fractions and the cross section of the production modes are varied as a function of $\kappa_{\lambda}$. In this case,the best fit value corresponds to $\kappa_{\lambda}$ = $2.7^{+2.0}_{-2.2}(^{+3.8}_{-4.3})$ and the expected value corresponds to $\kappa_{\lambda}$ = $1.0^{+5.4}_{-2.5}(^{+7.3}_{-4.3})$ for the 1$\sigma$(2$\sigma$) confidence interval.
Values of the negative log-profile-likelihood ($-2ln\Lambda$) as a function of $\kappa_{\lambda}$ evaluated for the combination of all the categories of the nonresonant search. The coupling of the Higgs boson to fermions and gauge bosons is set to SM values in the profile likelihood calculation. The Asimov data set is generated under the SM signal-plus-background hypothesis, $\kappa_{\lambda}$= 1. All systematic uncertainties, including the theoretical uncertainties on the di-Higgs boson production cross section, are included. The intersections of the solid curves and the horizontal dashed lines indicate the 1$\sigma$ and 2$\sigma$ confidence level intervals. The best fit value corresponds to $\kappa_{\lambda}$ = $2.8^{+2.0}_{-2.2}(^{+3.8}_{-4.3})$ for the 1$\sigma$(2$\sigma$) confidence interval. The expected value corresponds to $\kappa_{\lambda}$ = $1.0^{+5.5}_{-2.4}(^{+7.3}_{-4.2})$ for the 1$\sigma$(2$\sigma$) confidence interval. The dashed curves represent values of the negative log-profile-likelihood where the Higgs boson branching fractions and the cross section of the production modes are varied as a function of $\kappa_{\lambda}$. In this case,the best fit value corresponds to $\kappa_{\lambda}$ = $2.7^{+2.0}_{-2.2}(^{+3.8}_{-4.3})$ and the expected value corresponds to $\kappa_{\lambda}$ = $1.0^{+5.4}_{-2.5}(^{+7.3}_{-4.3})$ for the 1$\sigma$(2$\sigma$) confidence interval.
Minimum BDT value of the events passing the selection criteria of the resonant search. The combined BDT score is formed using as coefficients $C_{1}$ = 0.65 and $C_{2}$ = 1 − $C_{1}$. The selection efficiency for the resonant $X \rightarrow HH$ signal is also shown.
Minimum BDT value of the events passing the selection criteria of the resonant search. The combined BDT score is formed using as coefficients $C_{1}$ = 0.65 and $C_{2}$ = 1 − $C_{1}$. The selection efficiency for the resonant $X \rightarrow HH$ signal is also shown.
Minimum BDT value of the events passing the selection criteria of the resonant search. The combined BDT score is formed using as coefficients $C_{1}$ = 0.65 and $C_{2}$ = 1 − $C_{1}$. The selection efficiency for the resonant $X \rightarrow HH$ signal is also shown.
Minimum BDT value of the events passing the selection criteria of the resonant search. The combined BDT score is formed using as coefficients $C_{1}$ = 0.65 and $C_{2}$ = 1 − $C_{1}$. The selection efficiency for the resonant $X \rightarrow HH$ signal is also shown.
A search for supersymmetric partners of top quarks decaying as $\tilde{t}_1\to c\tilde\chi^0_1$ and supersymmetric partners of charm quarks decaying as $\tilde{c}_1\to c\tilde\chi^0_1$, where $\tilde\chi^0_1$ is the lightest neutralino, is presented. The search uses 36.1 ${\rm fb}^{-1}$ $pp$ collision data at a centre-of-mass energy of 13 TeV collected by the ATLAS experiment at the Large Hadron Collider and is performed in final states with jets identified as containing charm hadrons. Assuming a 100% branching ratio to $c\tilde\chi^0_1$, top and charm squarks with masses up to 850 GeV are excluded at 95% confidence level for a massless lightest neutralino. For $m_{\tilde{t}_1,\tilde{c}_1}-m_{\tilde\chi^0_1}
Acceptance for SR1 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for best expected CLS SR in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR1 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR2 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR1 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR2 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR3 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR2 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR3 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR4 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR3 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR4 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR5 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR4 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR5 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for best expected CLS SR in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for SR5 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Acceptance for best expected CLS SR in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR1 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for best expected CLS SR in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR1 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR2 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR1 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR2 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR3 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR2 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR3 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR4 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR3 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR4 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR5 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR4 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR5 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for best expected CLS SR in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for SR5 in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Detector efficiency for best expected CLS SR in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR1 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR1 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR1 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR1 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR1 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR1 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR2 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR2 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR2 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR2 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR2 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR2 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR3 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR3 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR3 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR3 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR3 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR3 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR4 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR4 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR4 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR4 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR4 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR4 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR5 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR5 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR5 expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR5 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR5 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
SR5 observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR1 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for the best expected SR in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR1 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR2 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR1 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR2 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR3 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR2 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR3 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR4 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR3 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR4 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR5 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR4 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR5 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for the best expected SR in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for SR5 in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Upper limits on signal cross sections and exclusion limits at 95% CL for the best expected SR in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Minimum branching ratio excluded at 95% CL, assuming no sensitivity for other decay possibilities, in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Minimum branching ratio excluded at 95% CL, assuming no sensitivity for other decay possibilities, in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
Minimum branching ratio excluded at 95% CL, assuming no sensitivity for other decay possibilities, in the $m(\tilde t_1/\tilde c_1)$-$m(\tilde\chi^0_1)$ plane for the stop/scharm pair production scenario.
The signal region with the best expected CLS value for each signal in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
The signal region with the best expected CLS value for each signal in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
The signal region with the best expected CLS value for each signal in the $\tilde{t}_1/\tilde{c}_1-\tilde{\chi}_1^0$ mass plane.
Expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$\Delta m$ plane for the stop/scharm pair production scenario.
Expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$\Delta m$ plane for the stop/scharm pair production scenario.
Expected exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$\Delta m$ plane for the stop/scharm pair production scenario.
Observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$\Delta m$ plane for the stop/scharm pair production scenario.
Observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$\Delta m$ plane for the stop/scharm pair production scenario.
Observed exclusion limit at 95% CL in the $m(\tilde t_1/\tilde c_1)$-$\Delta m$ plane for the stop/scharm pair production scenario.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR1. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR1. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR1. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR2. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR2. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR2. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR3. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR3. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR3. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR4. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR4. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR4. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR5. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR5. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Comparison between data and expectation after the background-only fit for the $E_{T}^{miss}$ distribution in SR5. The shaded band indicates detector-related systematic uncertainties and the statistical uncertainties of the MC samples, while the error bars on the data points indicate the data's statistical uncertainty. The final bin in each histogram includes the overflow. The lower panel shows the ratio of the data to the SM prediction after the background-only fit. The distribution is also shown for a representative signal point.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (450,425)$ GeV signal point for signal region SR1.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (450,425)$ GeV signal point for signal region SR1.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (450,425)$ GeV signal point for signal region SR1.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (500,420)$ GeV signal point for signal region SR2.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (500,420)$ GeV signal point for signal region SR2.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (500,420)$ GeV signal point for signal region SR2.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (500,350)$ GeV signal point for signal region SR3.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (500,350)$ GeV signal point for signal region SR3.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (500,350)$ GeV signal point for signal region SR3.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (600,350)$ GeV signal point for signal region SR4.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (600,350)$ GeV signal point for signal region SR4.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (600,350)$ GeV signal point for signal region SR4.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (900,1)$ GeV signal point for signal region SR5.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (900,1)$ GeV signal point for signal region SR5.
Cutflow for the $(m_{\tilde{t}}, m_{\tilde{\chi}}) = (900,1)$ GeV signal point for signal region SR5.
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