HEPData Search

Table 1

CalRatio triggers which were available during the LHC Run 2 data-taking, and corresponding integrated luminosity collected in each period. The high-ET CalRatio trigger with ET > 60 GeV was disabled in 2017 for instantaneous luminosities higher than 1.4 × 1034 cm-2 s-1. Two versions of the low-ET CalRatio trigger were used, with slight differences in their algorithms. The details are reported in Section 4.

Figure 2a

Trigger efficiency for simulated signal events as a function of the LLP pT for one of the low-ET signal samples for HLT CalRatio triggers seeded by the high-ET L1 triggers with ET thresholds of 60 GeV and 100 GeV and by the two versions of the low-ET L1 triggers. Only statistical uncertainties are shown.

Figure 2b

Trigger efficiency for simulated signal events as a function of the LLP pT for one of the high-ET signal samples for HLT CalRatio triggers seeded by the high-ET L1 triggers with ET thresholds of 60 GeV and 100 GeV and by the two versions of the low-ET L1 triggers. Only statistical uncertainties are shown.

Figure 3a

Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (Lxy) for LLPs decaying in the barrel (|η|<1.4) for one of the low-ET signal samples for HLT CalRatio triggers seeded by the high-ET L1 triggers with ET thresholds of 60 GeV and 100 GeV and by the two versions of the low-ET L1 triggers. Only statistical uncertainties are shown.

Figure 3b

Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (Lxy) for LLPs decaying in the barrel (|η|<1.4) for one of the high-ET signal samples for HLT CalRatio triggers seeded by the high-ET L1 triggers with ET thresholds of 60 GeV and 100 GeV and by the two versions of the low-ET L1 triggers. Only statistical uncertainties are shown.

Figure 5a

The NN output scores in the dijet control region for the low-ET 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.

Figure 5b

The NN output scores in the dijet control region for the low-ET 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.

Figure 5c

The NN output scores in the dijet control region for the high-ET 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.

Figure 5d

The NN output scores in the dijet control region for the high-ET 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.

Figure 7a

Distribution of the low-ET per-event BDT in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.

Figure 7b

Distribution of the high-ET per-event BDT outputs in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.

Table 3 Top

Sequential impact of each requirement on the number of events passing the selection for the high-ET selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.

Table 3 Bottom

Sequential impact of each requirement on the number of events passing the selection for the low-ET selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.

Table 4 Top

Application of the modified ABCD method to the final high-ET 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 NbkgA=(NbkgB· NbkgC)/NbkgD 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.

Table 4 Bottom

Application of the modified ABCD method to the final low-ET 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 NbkgA=(NbkgB· NbkgC)/NbkgD 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.

Figure 10a

95% CL expected and observed limits on the BR of SM Higgs bosons to pairs of neutral LLPs (BH→ ss), 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.

Figure 1a of Aux. Mat.

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-ET signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-ET trigger and by the two versions of the low-ET triggers.

Figure 1b of Aux. Mat.

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-ET signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-ET trigger and by the two versions of the low-ET triggers.

Figure 2a of Aux. Mat. 60_5