Barrel Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=200$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=400$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=600$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=1000$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for all Stealth SUSY benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow \nu b \bar{b}$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow cbs$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow lcb$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow \tau\tau\nu$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=100$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=125$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=200$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=400$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=600$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for $m_{\Phi}=1000$ GeV scalar benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for all Stealth SUSY benchmark samples. The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow \nu b \bar{b}$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow cbs$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow lcb$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Endcap Muon RoI Cluster trigger efficiencies (in %) for baryogenesis $\chi \rightarrow \tau\tau\nu$ benchmark samples ($m_{h}=125$ GeV). The trigger efficiency is defined as the fraction of LLPs selected by the Muon RoI Cluster trigger as a function of the LLP decay position. The trigger is efficient for hadronic decays of LLPs that occur anywhere from the outer regions of the HCal to the middle station of the MS. These efficiencies are obtained from the subset of events with only a single LLP decay in the muon spectrometer in order to ensure that the result of the trigger is due to a single burst of MS activity. The uncertainties shown are statistical only. The relative differences in efficiencies of the benchmark samples are a result of the different kinematics.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=100$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=125$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=200$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=400$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=600$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for $m_{\Phi}=1000$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for all Stealth SUSY benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow \nu b \bar{b}$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow cbs$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow lcb$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Barrel MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow \tau\tau\nu$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=100$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=125$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=200$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=400$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=600$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for $m_{\Phi}=1000$ GeV scalar benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for all Stealth SUSY benchmark samples. The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow \nu b \bar{b}$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow cbs$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow lcb$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Endcap MS vertex efficiencies (in %) for baryogenesis $\chi \rightarrow \tau\tau\nu$ benchmark samples ($m_{h}=125$ GeV). The vertex reconstruction efficiency is defined as the fraction of simulated LLP decays in the MS fiducial volume that match a reconstructed vertex ($\Delta R(\textrm{LLP,vertex}) = 0.4$) passing the baseline event selection and satisfying the vertex isolation criteria. A vertex is considered matched to a displaced decay if the vertex is within $\Delta R = 0.4$ of the simulated decay position. The MS vertex efficiency is parameterized as a function of the LLP decay position.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=5$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=5$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=5$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=8$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=8$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=8$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=15$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=15$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=15$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=25$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=25$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=25$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=40$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=40$ GeV scalar benchmark sample for 1MSVx strategy.

Upper 95% CL limits on $\sigma/\sigma_{\textrm{SM}}\times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=125$ GeV and $m_{s}=40$ GeV scalar benchmark sample for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=250$ GeV for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=500$ GeV for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=500$ GeV for 1MSVx strategy.

Upper 95% CL limits on $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=500$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=800$ GeV for 2MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=800$ GeV for 1MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=800$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1200$ GeV for 2MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1200$ GeV for 1MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1200$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1500$ GeV for 2MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1500$ GeV for 1MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=1500$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=2000$ GeV for 2MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=2000$ GeV for 1MSVx strategy.

Upper 95% CL limits $\sigma \times B_{\tilde{g} \rightarrow \tilde{S} g}$ as a function of the proper lifetime $c\tau$ for $m_{\tilde{g}}=2000$ GeV for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=100$ GeV and $m_{s}=8$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=100$ GeV and $m_{s}=25$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=200$ GeV and $m_{s}=8$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=200$ GeV and $m_{s}=25$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=200$ GeV and $m_{s}=50$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=400$ GeV and $m_{s}=50$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=400$ GeV and $m_{s}=100$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=600$ GeV and $m_{s}=50$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=600$ GeV and $m_{s}=150$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=1000$ GeV and $m_{s}=50$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=1000$ GeV and $m_{s}=150$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma \times B$ as a function of the proper lifetime $c\tau$ for $m_{\Phi}=1000$ GeV and $m_{s}=400$ GeV scalar benchmark sample for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \nu b \bar{b}$ channel with $m_{\chi}=100$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow c b s$ channel with $m_{\chi}=100$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 1MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for the combination of 1MSVx and 2MSVx strategies.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow l c b$ channel with $m_{\chi}=100$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \tau \tau \nu$ channel with $m_{\chi}=10$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \tau \tau \nu$ channel with $m_{\chi}=30$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \tau \tau \nu$ channel with $m_{\chi}=50$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

Upper 95% CL limits on $\sigma\times B$ as a function of the proper lifetime $c\tau$ for $\chi \rightarrow \tau \tau \nu$ channel with $m_{\chi}=100$ GeV ($m_{h}=125$ GeV) for 2MSVx strategy.

The expected upper limits on the production cross-section times the product of branching ratios for the benchmark signal scenario involving a scalar particle $X$ with narrow width decaying via $X\rightarrow aa\rightarrow 4\gamma$, $\sigma_X\times B(X\rightarrow aa)\times B(a\rightarrow\gamma\gamma)^2$. The limits for $m_{a}$ = 5 GeV and 10 GeV do not cover as large a range as the other mass points, since the region of interest is limited to $ m_{a} < 0.01 \times m_{X}$. Additionally, the expected limits are not provided for a small number of points, indicated with a hyphen, because of a technical failure with the computation.

The observed upper limits on the production cross-section times the product of branching ratios for the benchmark signal scenario involving a scalar particle $X$ with narrow width decaying via $X\rightarrow aa\rightarrow 6\pi^0$, $\sigma_X\times B(X\rightarrow aa)\times B(a\rightarrow 3\pi^0)^2$. The limits for $m_{a}$ = 5 GeV and 10 GeV do not cover as large a range as the other mass points, since the region of interest is limited to $ m_{a} < 0.01 \times m_{X}$.

The expected upper limits on the production cross-section times the product of branching ratios for the benchmark signal scenario involving a scalar particle $X$ with narrow width decaying via $X\rightarrow aa\rightarrow 6\pi^0$, $\sigma_X\times B(X\rightarrow aa)\times B(a\rightarrow 3\pi^0)^2$. The limits for $m_{a}$ = 5 GeV and 10 GeV do not cover as large a range as the other mass points, since the region of interest is limited to $ m_{a} < 0.01 \times m_{X}$. Additionally, the expected limits are not provided for a small number of points, indicated with a hyphen, because of a technical failure with the computation.

Observed 95% CL upper limits on the visible cross section as a function of $m_X$ and the fraction of events in the low-$\Delta E$ category.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 0.1 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 0.5 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 0.7 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 1 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 2 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 5 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 10 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 0.5 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 0.7 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 1 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 2 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 5 GeV.

Selection efficiency for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 10 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 0.1 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 0.5 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 0.7 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 1 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 2 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 5 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 2\gamma$ with $m_a$ = 10 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 0.5 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 0.7 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 1 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 2 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 5 GeV.

Fraction of reconstructed photons with a value of shower shape variable $\Delta E$ lower than the threshold, for reconstructed photons originating from the decay $a\rightarrow 3\pi^0\rightarrow 6\gamma$ with $m_a$ = 10 GeV.

Selection efficiency for photons originating from the BSM process $X\rightarrow\gamma\gamma$, where the $X$ particle is a high-mass narrow-width scalar particle originating from the gluon--gluon fusion process.

Fraction of photons with a value of shower shape variable $\Delta E$ lower than the threshold, for photons originating from the BSM process $X\rightarrow\gamma\gamma$, where the $X$ particle is a high-mass narrow-width scalar particle originating from the gluon--gluon fusion process.

Data from Fig 3b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.

Data from Fig 3c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. The uncertainties are applied symmetrically, though the cross section cannot go below zero in the first bin.

Data from Fig 3c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. The uncertainties are applied symmetrically, though the cross section cannot go below zero in the first bin.

Data from Fig 4 and Fig 8a-16a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for beta = 0, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 4 and FigAux 8a-16a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for beta = 0, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 4 and Fig 8b-16b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 4 and FigAux 8b-16b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 8c-16c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 8c-16c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6a. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 0. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6a. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 0. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6b. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 1. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6b. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 1. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6c. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 2. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6c. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 2. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 7a. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 0, inclusive in $p_T$.

Data from FigAux 7a. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 0, inclusive in $p_T$.

Data from Fig 7b. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 1, inclusive in $p_T$.

Data from FigAux 7b. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 1, inclusive in $p_T$.

Data from Fig 7c. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 2, inclusive in $p_T$.

Data from FigAux 7c. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 2, inclusive in $p_T$.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the inclusive jet multiplicity.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the inclusive jet multiplicity.

Differential cross section for the production of W bosons as a function of H_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of H_T for events with N_jets &ge; 1.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the H_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the H_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the H_T for events with N_jets &ge; 1.

Differential cross section for the production of W bosons as a function of the W p_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the W p_T for events with N_jets &ge; 1.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the W p_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the W p_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the W p_T for events with N_jets &ge; 1.

Differential cross section for the production of W bosons as a function of the leading jet p_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the leading jet p_T for events with N_jets &ge; 1.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the leading jet p_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the leading jet p_T for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the leading jet p_T for events with N_jets &ge; 1.

Differential cross section for the production of W bosons as a function of the leading jet rapidity for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the leading jet rapidity for events with N_jets &ge; 1.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the leading jet rapidity for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the leading jet rapidity for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the leading jet rapidity for events with N_jets &ge; 1.

Differential cross section for the production of W bosons as a function of second leading jet p_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of second leading jet p_T for events with N_jets &ge; 2.

Differential cross section for the production of W bosons as a function of second leading jet rapidity for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of second leading jet rapidity for events with N_jets &ge; 2.

Differential cross section for the production of W bosons as a function of &Delta; R_jet1,jet2 for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of &Delta; R_jet1,jet2 for events with N_jets &ge; 2.

Differential cross section for the production of W bosons as a function of dijet invariant mass for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of dijet invariant mass for events with N_jets &ge; 2.

Cross section for the production of W bosons as a function of exclusive jet multiplicity.

Statistical correlation between bins in data for the cross section for the production of W bosons as a function of exclusive jet multiplicity.

Differential cross section for the production of W bosons as a function of the H_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the H_T for events with N_jets &ge; 2.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the H_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the H_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the H_T for events with N_jets &ge; 2.

Differential cross section for the production of W bosons as a function of the W p_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the W p_T for events with N_jets &ge; 2.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the W p_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the W p_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the W p_T for events with N_jets &ge; 2.

Differential cross section for the production of W bosons as a function of the leading jet p_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the leading jet p_T for events with N_jets &ge; 2.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the leading jet p_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the leading jet p_T for events with N_jets &ge; 2.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the leading jet p_T for events with N_jets &ge; 2.

Differential cross section for the production of W bosons as a function of the electron &eta; for events with N_jets &ge; 0.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the electron &eta; for events with N_jets &ge; 0.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the electron &eta; for events with N_jets &ge; 0.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the electron &eta; for events with N_jets &ge; 0.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the electron &eta; for events with N_jets &ge; 0.

Differential cross section for the production of W bosons as a function of the electron &eta; for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross section for the production of W bosons as a function of the electron &eta; for events with N_jets &ge; 1.

Differential cross sections for the production of W⁺ bosons, W^- bosons and the W⁺/W^- cross section ratio as a function of the electron &eta; for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W⁺ bosons as a function of the electron &eta; for events with N_jets &ge; 1.

Statistical correlation between bins in data for the differential cross sections for the production of W^- bosons as a function of the electron &eta; for events with N_jets &ge; 1.

List of experimentally considered systematic uncertainties for the W+jets cross section measurement

Non-perturbative corrections for the cross section for the production of W bosons for different inclusive jet multiplicities.

Non-perturbative corrections for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the inclusive jet multiplicity.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of H_T for events with N_jets &ge; 1.

Non-perturbative corrections for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the H_T for events with N_jets &ge; 1.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the W p_T for events with N_jets &ge; 1.

Non-perturbative corrections for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the W p_T for events with N_jets &ge; 1.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the leading jet p_T for events with N_jets &ge; 1.

Non-perturbative corrections for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the leading jet p_T for events with N_jets &ge; 1.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the leading jet rapidity for events with N_jets &ge; 1.

Non-perturbative corrections for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the leading jet rapidity for events with N_jets &ge; 1.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of second leading jet p_T for events with N_jets &ge; 2.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of second leading jet rapidity for events with N_jets &ge; 2.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of &Delta; R_jet1,jet2 for events with N_jets &ge; 2.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of dijet invariant mass for events with N_jets &ge; 2.

Non-perturbative corrections for the cross section for the production of W bosons as a function of exclusive jet multiplicity.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the H_T for events with N_jets &ge; 2.

Non-perturbative corrections for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the H_T for events with N_jets &ge; 2.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the W p_T for events with N_jets &ge; 2.

Non-perturbative corrections for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the W p_T for events with N_jets &ge; 2.

Non-perturbative corrections for the differential cross section for the production of W bosons as a function of the leading jet p_T for events with N_jets &ge; 2.

Non-perturbative corrections for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the leading jet p_T for events with N_jets &ge; 2.

NNLO/NLO k-factors determined with NNLO Njetti for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the H_T for events with N_jets &ge; 1. These numbers were obtained with code described in Phys. Rev. Lett. 115 (2015) 062002 [arXiv:1504.02131].

NNLO/NLO k-factors determined with NNLO Njetti for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the W p_T for events with N_jets &ge; 1. These numbers were obtained with code described in Phys. Rev. Lett. 115 (2015) 062002 [arXiv:1504.02131].

NNLO/NLO k-factors determined with NNLO Njetti for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the leading jet p_T for events with N_jets &ge; 1. These numbers were obtained with code described in Phys. Rev. Lett. 115 (2015) 062002 [arXiv:1504.02131].

NNLO/NLO k-factors determined with NNLO Njetti for the differential cross sections for the production of W⁺ bosons and W^- bosons as a function of the leading jet rapidity for events with N_jets &ge; 1. These numbers were obtained with code described in Phys. Rev. Lett. 115 (2015) 062002 [arXiv:1504.02131].

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eleven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eleven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eleven jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with twelve-or-more jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with twelve-or-more jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with twelve-or-more jets in the different b-tag multiplicity bins for the 40 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 60 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with five jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with six jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with seven jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with eight jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with nine jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

The expected background and observed data with ten-or-more jets in the different b-tag multiplicity bins for the 80 GeV jet pT threshold. The background shown is estimated by including all bins in the fit.

Observed exclusion contours on the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Observed exclusion contours on the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Observed exclusion contours on the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Expected exclusion contours on the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Expected exclusion contours on the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Expected exclusion contours on the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Observed exclusion contours on the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Observed exclusion contours on the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Observed exclusion contours on the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Expected exclusion contours on the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Expected exclusion contours on the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Expected exclusion contours on the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Observed exclusion contours on the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Observed exclusion contours on the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Observed exclusion contours on the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Expected exclusion contours on the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Expected exclusion contours on the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Expected exclusion contours on the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Observed exclusion contours on the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Observed exclusion contours on the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Observed exclusion contours on the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Expected exclusion contours on the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Expected exclusion contours on the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Expected exclusion contours on the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Observed exclusion contours on the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Observed exclusion contours on the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Observed exclusion contours on the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Expected exclusion contours on the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Expected exclusion contours on the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Expected exclusion contours on the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and neutralino masses in a model where the gluino decays via a virtual top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino --> uds) via the RPV coupling lambda''_112.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Observed upper limits on the model cross-section in units of pb as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Observed upper limits on the model cross-section in units of pb as a function of the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Observed upper limits on the model cross-section in units of pb as a function of the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Observed upper limits on the model cross-section in units of pb as a function of the stop and neutralino masses in a model where the stop decays to a third-generation quark and a higgsino, which decays via the RPV coupling lambda''_323.

Observed upper limits on the model cross-section in units of pb as a function of the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Observed upper limits on the model cross-section in units of pb as a function of the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Observed upper limits on the model cross-section in units of pb as a function of the stop and neutralino masses in a model where the stop decays to a top and a bino-like neutralino, which decays via the RPV coupling lambda''_323.

Acceptance as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Acceptance as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Acceptance as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Efficiency as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Efficiency as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Efficiency as a function of the gluino and stop masses in a model where the gluino decays to a top quark and a top squark, with the top squark decaying to an s-quark and a b-quark via a non-zero lambda''_323 RPV coupling.

Acceptance as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Acceptance as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Acceptance as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Efficiency as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Efficiency as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Efficiency as a function of the gluino and neutralino masses in a model with a gluino decaying to two light quarks (q=u,d,s,c) and the neutralino, which then decays to two light quarks and a charged lepton or a neutrino.

Cut flow for a model of gluino pair production where the gluino decays to two (u, d, s, c) quarks and the neutralino, which then decays to two (u, d, s, c) quarks and a lepton via a lambda' RPV coupling, where each RPV decay can produce any of the four first and second generation leptons (e, mu, nu_e, nu_mu) with equal probability (m_gluino = 1800 GeV, m_neutralino = 900 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where the gluino decays to two (u, d, s, c) quarks and the neutralino, which then decays to two (u, d, s, c) quarks and a lepton via a lambda' RPV coupling, where each RPV decay can produce any of the four first and second generation leptons (e, mu, nu_e, nu_mu) with equal probability (m_gluino = 1800 GeV, m_neutralino = 900 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where the gluino decays to two (u, d, s, c) quarks and the neutralino, which then decays to two (u, d, s, c) quarks and a lepton via a lambda' RPV coupling, where each RPV decay can produce any of the four first and second generation leptons (e, mu, nu_e, nu_mu) with equal probability (m_gluino = 1800 GeV, m_neutralino = 900 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays to a top quark and a top squark, with the top squark decaying to an s- and a b- quark via a non-zero lambda''_323 RPV coupling (m_gluino = 1600 GeV, m_stop = 1000 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays to a top quark and a top squark, with the top squark decaying to an s- and a b- quark via a non-zero lambda''_323 RPV coupling (m_gluino = 1600 GeV, m_stop = 1000 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays to a top quark and a top squark, with the top squark decaying to an s- and a b- quark via a non-zero lambda''_323 RPV coupling (m_gluino = 1600 GeV, m_stop = 1000 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays via an off-shell top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino -> uds) via the RPV coupling lambda''_112 (m_gluino = 2000 GeV, m_neutralino = 941 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays via an off-shell top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino -> uds) via the RPV coupling lambda''_112 (m_gluino = 2000 GeV, m_neutralino = 941 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of gluino pair production where each gluino decays via an off-shell top squark to two top quarks and the lightest neutralino, with the neutralino decaying to three light quarks (neutralino -> uds) via the RPV coupling lambda''_112 (m_gluino = 2000 GeV, m_neutralino = 941 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of right-handed top squark pair production with the top squark decaying to the lightest supersymmetric particle (LSP) which is considered to be purely higgsino. The higgsino-like LSP decays through the non-zero RPV coupling lambda''_323 (m_stop = 975 GeV, m_neutralino = 600 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of right-handed top squark pair production with the top squark decaying to the lightest supersymmetric particle (LSP) which is considered to be purely higgsino. The higgsino-like LSP decays through the non-zero RPV coupling lambda''_323 (m_stop = 975 GeV, m_neutralino = 600 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

Cut flow for a model of right-handed top squark pair production with the top squark decaying to the lightest supersymmetric particle (LSP) which is considered to be purely higgsino. The higgsino-like LSP decays through the non-zero RPV coupling lambda''_323 (m_stop = 975 GeV, m_neutralino = 600 GeV). The events are skimmed by requiring at least one electron or muon that satisfies very loose identification criteria, where the lepton satisfies pT > 25 GeV. The efficiency of this cut is considered in the quoted efficiency of the lepton trigger requirement. Selections with negligible inefficiencies on the given sample, such as data quality requirements, are not displayed.

<b>Kinematics 2</b> Kinematic distributions after the background-only fit showing the data as well as the expected background in the inclusive electroweakino SR&#8467;&#8467;-m_{&#8467;&#8467;} [1, 60] (top) and slepton SR&#8467;&#8467;-m_T2¹⁰⁰ [100, &infin;] (bottom) signal regions. The arrow in the E_T^miss/H_T^lep variables indicates the minimum value of the requirement imposed in the final SR selection. The m_{&#8467;&#8467;} and m_T2 distributions (right) have all the SR requirements applied. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The uncertainty bands plotted include all statistical and systematic uncertainties. The last bin includes overflow. The dashed lines represent benchmark signal samples corresponding to the Higgsino H&#771; and slepton &#8467;&#771; simplified models. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 3</b> Kinematic distributions after the background-only fit showing the data as well as the expected background in the inclusive electroweakino SR&#8467;&#8467;-m_{&#8467;&#8467;} [1, 60] (top) and slepton SR&#8467;&#8467;-m_T2¹⁰⁰ [100, &infin;] (bottom) signal regions. The arrow in the E_T^miss/H_T^lep variables indicates the minimum value of the requirement imposed in the final SR selection. The m_{&#8467;&#8467;} and m_T2 distributions (right) have all the SR requirements applied. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The uncertainty bands plotted include all statistical and systematic uncertainties. The last bin includes overflow. The dashed lines represent benchmark signal samples corresponding to the Higgsino H&#771; and slepton &#8467;&#771; simplified models. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 3</b> Kinematic distributions after the background-only fit showing the data as well as the expected background in the inclusive electroweakino SR&#8467;&#8467;-m_{&#8467;&#8467;} [1, 60] (top) and slepton SR&#8467;&#8467;-m_T2¹⁰⁰ [100, &infin;] (bottom) signal regions. The arrow in the E_T^miss/H_T^lep variables indicates the minimum value of the requirement imposed in the final SR selection. The m_{&#8467;&#8467;} and m_T2 distributions (right) have all the SR requirements applied. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The uncertainty bands plotted include all statistical and systematic uncertainties. The last bin includes overflow. The dashed lines represent benchmark signal samples corresponding to the Higgsino H&#771; and slepton &#8467;&#771; simplified models. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 4</b> Kinematic distributions after the background-only fit showing the data as well as the expected background in the inclusive electroweakino SR&#8467;&#8467;-m_{&#8467;&#8467;} [1, 60] (top) and slepton SR&#8467;&#8467;-m_T2¹⁰⁰ [100, &infin;] (bottom) signal regions. The arrow in the E_T^miss/H_T^lep variables indicates the minimum value of the requirement imposed in the final SR selection. The m_{&#8467;&#8467;} and m_T2 distributions (right) have all the SR requirements applied. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The uncertainty bands plotted include all statistical and systematic uncertainties. The last bin includes overflow. The dashed lines represent benchmark signal samples corresponding to the Higgsino H&#771; and slepton &#8467;&#771; simplified models. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 4</b> Kinematic distributions after the background-only fit showing the data as well as the expected background in the inclusive electroweakino SR&#8467;&#8467;-m_{&#8467;&#8467;} [1, 60] (top) and slepton SR&#8467;&#8467;-m_T2¹⁰⁰ [100, &infin;] (bottom) signal regions. The arrow in the E_T^miss/H_T^lep variables indicates the minimum value of the requirement imposed in the final SR selection. The m_{&#8467;&#8467;} and m_T2 distributions (right) have all the SR requirements applied. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The uncertainty bands plotted include all statistical and systematic uncertainties. The last bin includes overflow. The dashed lines represent benchmark signal samples corresponding to the Higgsino H&#771; and slepton &#8467;&#771; simplified models. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Exclusion 1 (exp)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with pm1&sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with pm1&sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct Higgsino (top) and wino (bottom) production. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive the limit, which is projected into the &Delta; m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) vs. m(&chi;&#771;₂⁰) plane. For Higgsino production, the chargino &chi;&#771;₁^pm mass is assumed to be halfway between the two lightest neutralino masses, while m(&chi;&#771;₂⁰) = m(&chi;&#771;₁^pm) is assumed for the wino--bino model. The gray regions denote the lower chargino mass limit from LEP [20]. The blue region in the lower plot indicates the limit from the 2&#8467;+3&#8467; combination of ATLAS Run 1 [41,42].

<b>Exclusion 1 (exp)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with pm1&sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with pm1&sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct Higgsino (top) and wino (bottom) production. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive the limit, which is projected into the &Delta; m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) vs. m(&chi;&#771;₂⁰) plane. For Higgsino production, the chargino &chi;&#771;₁^pm mass is assumed to be halfway between the two lightest neutralino masses, while m(&chi;&#771;₂⁰) = m(&chi;&#771;₁^pm) is assumed for the wino--bino model. The gray regions denote the lower chargino mass limit from LEP [20]. The blue region in the lower plot indicates the limit from the 2&#8467;+3&#8467; combination of ATLAS Run 1 [41,42].

<b>Exclusion 1 (obs)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with pm1&sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with pm1&sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct Higgsino (top) and wino (bottom) production. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive the limit, which is projected into the &Delta; m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) vs. m(&chi;&#771;₂⁰) plane. For Higgsino production, the chargino &chi;&#771;₁^pm mass is assumed to be halfway between the two lightest neutralino masses, while m(&chi;&#771;₂⁰) = m(&chi;&#771;₁^pm) is assumed for the wino--bino model. The gray regions denote the lower chargino mass limit from LEP [20]. The blue region in the lower plot indicates the limit from the 2&#8467;+3&#8467; combination of ATLAS Run 1 [41,42].

<b>Exclusion 1 (obs)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with pm1&sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with pm1&sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct Higgsino (top) and wino (bottom) production. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive the limit, which is projected into the &Delta; m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) vs. m(&chi;&#771;₂⁰) plane. For Higgsino production, the chargino &chi;&#771;₁^pm mass is assumed to be halfway between the two lightest neutralino masses, while m(&chi;&#771;₂⁰) = m(&chi;&#771;₁^pm) is assumed for the wino--bino model. The gray regions denote the lower chargino mass limit from LEP [20]. The blue region in the lower plot indicates the limit from the 2&#8467;+3&#8467; combination of ATLAS Run 1 [41,42].

<b>Exclusion 2 (exp)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with pm1&sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with pm1&sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct Higgsino (top) and wino (bottom) production. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive the limit, which is projected into the &Delta; m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) vs. m(&chi;&#771;₂⁰) plane. For Higgsino production, the chargino &chi;&#771;₁^pm mass is assumed to be halfway between the two lightest neutralino masses, while m(&chi;&#771;₂⁰) = m(&chi;&#771;₁^pm) is assumed for the wino--bino model. The gray regions denote the lower chargino mass limit from LEP [20]. The blue region in the lower plot indicates the limit from the 2&#8467;+3&#8467; combination of ATLAS Run 1 [41,42].

<b>Exclusion 2 (exp)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with pm1&sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with pm1&sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct Higgsino (top) and wino (bottom) production. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive the limit, which is projected into the &Delta; m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) vs. m(&chi;&#771;₂⁰) plane. For Higgsino production, the chargino &chi;&#771;₁^pm mass is assumed to be halfway between the two lightest neutralino masses, while m(&chi;&#771;₂⁰) = m(&chi;&#771;₁^pm) is assumed for the wino--bino model. The gray regions denote the lower chargino mass limit from LEP [20]. The blue region in the lower plot indicates the limit from the 2&#8467;+3&#8467; combination of ATLAS Run 1 [41,42].

<b>Exclusion 2 (obs)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with pm1&sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with pm1&sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct Higgsino (top) and wino (bottom) production. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive the limit, which is projected into the &Delta; m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) vs. m(&chi;&#771;₂⁰) plane. For Higgsino production, the chargino &chi;&#771;₁^pm mass is assumed to be halfway between the two lightest neutralino masses, while m(&chi;&#771;₂⁰) = m(&chi;&#771;₁^pm) is assumed for the wino--bino model. The gray regions denote the lower chargino mass limit from LEP [20]. The blue region in the lower plot indicates the limit from the 2&#8467;+3&#8467; combination of ATLAS Run 1 [41,42].

<b>Exclusion 2 (obs)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with pm1&sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with pm1&sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct Higgsino (top) and wino (bottom) production. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive the limit, which is projected into the &Delta; m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) vs. m(&chi;&#771;₂⁰) plane. For Higgsino production, the chargino &chi;&#771;₁^pm mass is assumed to be halfway between the two lightest neutralino masses, while m(&chi;&#771;₂⁰) = m(&chi;&#771;₁^pm) is assumed for the wino--bino model. The gray regions denote the lower chargino mass limit from LEP [20]. The blue region in the lower plot indicates the limit from the 2&#8467;+3&#8467; combination of ATLAS Run 1 [41,42].

<b>Exclusion 3 (exp)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with &plusmn; 1 &sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with &plusmn; 1 &sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct slepton production. A fit of slepton signals to the m_T2¹⁰⁰ spectrum is used to derive the limit, which is projected into the &Delta; m(&#8467;&#771;, &chi;&#771;₁⁰) vs. m(&#8467;&#771;) plane. Slepton &#8467;&#771; refers to the scalar partners of left- and right-handed electrons and muons, which are assumed to be fourfold mass degenerate m(e&#771;_L) = m(e&#771;_R) = m(&mu;&#771;_L) = m(&mu;&#771;_R). The gray region is the e&#771;_R limit from LEP [20,24], while the blue region is the fourfold mass degenerate slepton limit from ATLAS Run 1 [41].

<b>Exclusion 3 (exp)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with &plusmn; 1 &sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with &plusmn; 1 &sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct slepton production. A fit of slepton signals to the m_T2¹⁰⁰ spectrum is used to derive the limit, which is projected into the &Delta; m(&#8467;&#771;, &chi;&#771;₁⁰) vs. m(&#8467;&#771;) plane. Slepton &#8467;&#771; refers to the scalar partners of left- and right-handed electrons and muons, which are assumed to be fourfold mass degenerate m(e&#771;_L) = m(e&#771;_R) = m(&mu;&#771;_L) = m(&mu;&#771;_R). The gray region is the e&#771;_R limit from LEP [20,24], while the blue region is the fourfold mass degenerate slepton limit from ATLAS Run 1 [41].

<b>Exclusion 3 (obs)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with &plusmn; 1 &sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with &plusmn; 1 &sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct slepton production. A fit of slepton signals to the m_T2¹⁰⁰ spectrum is used to derive the limit, which is projected into the &Delta; m(&#8467;&#771;, &chi;&#771;₁⁰) vs. m(&#8467;&#771;) plane. Slepton &#8467;&#771; refers to the scalar partners of left- and right-handed electrons and muons, which are assumed to be fourfold mass degenerate m(e&#771;_L) = m(e&#771;_R) = m(&mu;&#771;_L) = m(&mu;&#771;_R). The gray region is the e&#771;_R limit from LEP [20,24], while the blue region is the fourfold mass degenerate slepton limit from ATLAS Run 1 [41].

<b>Exclusion 3 (obs)</b> Expected 95% CL exclusion sensitivity (blue dashed line) with &plusmn; 1 &sigma;_exp (yellow band) from experimental systematic uncertainties and observed limits (red solid line) with &plusmn; 1 &sigma;_theory (dotted red line) from signal cross-section uncertainties for simplified models of direct slepton production. A fit of slepton signals to the m_T2¹⁰⁰ spectrum is used to derive the limit, which is projected into the &Delta; m(&#8467;&#771;, &chi;&#771;₁⁰) vs. m(&#8467;&#771;) plane. Slepton &#8467;&#771; refers to the scalar partners of left- and right-handed electrons and muons, which are assumed to be fourfold mass degenerate m(e&#771;_L) = m(e&#771;_R) = m(&mu;&#771;_L) = m(&mu;&#771;_R). The gray region is the e&#771;_R limit from LEP [20,24], while the blue region is the fourfold mass degenerate slepton limit from ATLAS Run 1 [41].

<b>Upper Limits 1</b> The first two columns present observed (N_obs) and expected (N_exp) event yields in the inclusive signal regions. The latter are obtained by the background-only fit of the control regions, and the errors include both statistical and systematic uncertainties. The next two columns show the observed 95% CL upper limits on the visible cross-section (&lang;&epsilon;&sigma;&rang;_obs⁹⁵) and on the number of signal events (S_obs⁹⁵). The fifth column (S_exp⁹⁵) shows what the 95% CL upper limit on the number of signal events would be, given an observed number of events equal to the expected number (and +- 1 &sigma; deviations from the expectation) of background events. The last column indicates the discovery p-value (p(s = 0)), which is capped at 0.5.

<b>Upper Limits 1</b> The first two columns present observed (N_obs) and expected (N_exp) event yields in the inclusive signal regions. The latter are obtained by the background-only fit of the control regions, and the errors include both statistical and systematic uncertainties. The next two columns show the observed 95% CL upper limits on the visible cross-section (&lang;&epsilon;&sigma;&rang;_obs⁹⁵) and on the number of signal events (S_obs⁹⁵). The fifth column (S_exp⁹⁵) shows what the 95% CL upper limit on the number of signal events would be, given an observed number of events equal to the expected number (and +- 1 &sigma; deviations from the expectation) of background events. The last column indicates the discovery p-value (p(s = 0)), which is capped at 0.5.

<b>Cutflow 1</b> Observed event yields and exclusion fit results with the signal strength parameter set to zero for the exclusive electroweakino and slepton signal regions. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. Uncertainties in the fitted background estimates combine statistical and systematic uncertainties.

<b>Cutflow 1</b> Observed event yields and exclusion fit results with the signal strength parameter set to zero for the exclusive electroweakino and slepton signal regions. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. Uncertainties in the fitted background estimates combine statistical and systematic uncertainties.

<b>Acceptances 1</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 1</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 2</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 2</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 3</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 3</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 4</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 4</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 5</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 5</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 6</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 6</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 7</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 7</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^&plusmn; production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 8</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 8</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 9</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 9</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 10</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 10</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 11</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 11</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 12</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 12</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 13</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 13</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 14</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 14</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 15</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 15</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 16</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 16</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 17</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 17</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 18</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 18</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 19</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 19</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 20</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 20</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 21</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 21</b> Truth acceptances for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 22</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 22</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 23</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 23</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 24</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 24</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 25</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 25</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 26</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 26</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 27</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 27</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 28</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 28</b> Truth acceptances for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Numbers overlaid on the mass planes are the acceptance &times; 10⁴.

<b>Acceptances 29</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 29</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 30</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 30</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 31</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 31</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 32</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 32</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 33</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 33</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 34</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Acceptances 34</b> Truth acceptances for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Numbers overlaid on the mass planes are the acceptance &times; 10³.

<b>Efficiencies 1</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 1</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 2</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 2</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 3</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 3</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 4</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 4</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 5</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 5</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 6</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 6</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 7</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 7</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁺ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 8</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 8</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 9</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 9</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 10</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 10</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 11</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 11</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 12</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 12</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 13</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 13</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 14</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 14</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 15</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 15</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 16</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 16</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 17</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 17</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 18</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 18</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 19</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 19</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 20</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 20</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 21</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 21</b> Efficiencies for the Higgsino &chi;&#771;₂⁰&chi;&#771;₁⁰ production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 22</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 22</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 23</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 23</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 24</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 24</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 25</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 25</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 26</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 26</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 27</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 27</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 28</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 28</b> Efficiencies for the Higgsino &chi;&#771;₁⁺&chi;&#771;₁^- production process in the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 29</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 29</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 30</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 30</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 31</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 31</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 32</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 32</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 33</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 33</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 34</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Efficiencies 34</b> Efficiencies for the &#8467;&#771;&#8467;&#771; production in the inclusive SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Efficiencies are computed as the ``acceptance times efficiency" divided by the acceptance. The black line indicates the maximum allowed value of &Delta; m or m_T2 for the inclusive signal region under study.

<b>Cross-Sections 1</b> Cross-sections of the Higgsino signal grid for each production process denoted in the caption.

<b>Cross-Sections 1</b> Cross-sections of the Higgsino signal grid for each production process denoted in the caption.

<b>Cross-Sections 2</b> Cross-sections of the Higgsino signal grid for each production process denoted in the caption.

<b>Cross-Sections 2</b> Cross-sections of the Higgsino signal grid for each production process denoted in the caption.

<b>Cross-Sections 3</b> Cross-sections of the Higgsino signal grid for each production process denoted in the caption.

<b>Cross-Sections 3</b> Cross-sections of the Higgsino signal grid for each production process denoted in the caption.

<b>Cross-Sections 4</b> Cross-sections of the Higgsino signal grid for each production process denoted in the caption.

<b>Cross-Sections 4</b> Cross-sections of the Higgsino signal grid for each production process denoted in the caption.

<b>Cross-Sections 5</b> Cross-sections of the wino--bino signal grid for each production process in the caption.

<b>Cross-Sections 5</b> Cross-sections of the wino--bino signal grid for each production process in the caption.

<b>Cross-Sections 6</b> Cross-sections of the wino--bino signal grid for each production process in the caption.

<b>Cross-Sections 6</b> Cross-sections of the wino--bino signal grid for each production process in the caption.

<b>Cross-Sections 7</b> Total cross-sections of the slepton simplified model signal grid. Slepton refers to a the scalar partners of the left- and right-handed electrons and muons, which are assumed to be mass degenerate m(e&#771;_L) = m(e&#771;_R) = m(&mu;&#771;_L) = m(&mu;&#771;_R).

<b>Cross-Sections 7</b> Total cross-sections of the slepton simplified model signal grid. Slepton refers to a the scalar partners of the left- and right-handed electrons and muons, which are assumed to be mass degenerate m(e&#771;_L) = m(e&#771;_R) = m(&mu;&#771;_L) = m(&mu;&#771;_R).

<b>Kinematics 5</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 5</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 6</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 6</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 7</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 7</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 8</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 8</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 9</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 9</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 10</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 10</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 11</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 11</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 12</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 12</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 13</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 13</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 14</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 14</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 15</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Kinematics 15</b> Distributions after the background-only fit of kinematic variables used to define selections common to all signal regions, i.e. not including requirements specific to the electroweakino or slepton SR definitions. Blue arrows in the upper panel denote the final requirement used to define the common SR, otherwise all selections are applied. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. The first (last) bin includes underflow (overflow). Benchmark Higgsino H&#771; and slepton &#8467;&#771; signals are overlaid as dashed lines. Orange arrows in the Data/SM panel indicate values that are beyond the y-axis range.

<b>Upper Limits 2</b> Numbers show 95% CL model-dependent upper limits on the inclusive Higgsino signal cross-sections.

<b>Upper Limits 2</b> Numbers show 95% CL model-dependent upper limits on the inclusive Higgsino signal cross-sections.

<b>Upper Limits 3</b> Numbers show 95% CL model-dependent upper limits on the inclusive Higgsino signal cross-sections.

<b>Upper Limits 3</b> Numbers show 95% CL model-dependent upper limits on the inclusive Higgsino signal cross-sections.

<b>Upper Limits 4</b> Numbers show 95% CL model-dependent upper limits on the inclusive signal cross-sections of the wino--bino model.

<b>Upper Limits 4</b> Numbers show 95% CL model-dependent upper limits on the inclusive signal cross-sections of the wino--bino model.

<b>Upper Limits 5</b> Numbers show 95% CL model-dependent upper limits on the inclusive signal cross-sections of the wino--bino model.

<b>Upper Limits 5</b> Numbers show 95% CL model-dependent upper limits on the inclusive signal cross-sections of the wino--bino model.

<b>Upper Limits 6</b> Numbers show the 95% CL model-dependent upper limits on the slepton signal cross-sections, assuming a fourfold mass degeneracy m(e&#771;_L,R) = m(&mu;&#771;_L,R).

<b>Upper Limits 6</b> Numbers show the 95% CL model-dependent upper limits on the slepton signal cross-sections, assuming a fourfold mass degeneracy m(e&#771;_L,R) = m(&mu;&#771;_L,R).

<b>Upper Limits 7</b> Numbers show the 95% CL model-dependent upper limits on the slepton signal cross-sections, assuming a fourfold mass degeneracy m(e&#771;_L,R) = m(&mu;&#771;_L,R).

<b>Upper Limits 7</b> Numbers show the 95% CL model-dependent upper limits on the slepton signal cross-sections, assuming a fourfold mass degeneracy m(e&#771;_L,R) = m(&mu;&#771;_L,R).

<b>Upper Limits 8</b> Expected and observed 95% CL cross-section upper limits as a function of the universal gaugino mass m_1/2 for the NUHM2 model. The gray numbers indicate the values of the observed limit. The green and yellow bands around the expected limit indicate the &plusmn; 1&sigma; and &plusmn; 2&sigma; uncertainties, respectively. The expected signal production cross-sections as well as the associated uncertainty are indicated with the blue solid and dashed lines. The lower x-axis indicates the difference between the &chi;&#771;₂⁰ and &chi;&#771;₁⁰ masses for different values of m_1/2. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive this limit.

<b>Upper Limits 8</b> Expected and observed 95% CL cross-section upper limits as a function of the universal gaugino mass m_1/2 for the NUHM2 model. The gray numbers indicate the values of the observed limit. The green and yellow bands around the expected limit indicate the &plusmn; 1&sigma; and &plusmn; 2&sigma; uncertainties, respectively. The expected signal production cross-sections as well as the associated uncertainty are indicated with the blue solid and dashed lines. The lower x-axis indicates the difference between the &chi;&#771;₂⁰ and &chi;&#771;₁⁰ masses for different values of m_1/2. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive this limit.

<b>Upper Limits 9</b> Expected and observed 95% CL cross-section upper limits as a function of the universal gaugino mass m_1/2 for the NUHM2 model. The gray numbers indicate the values of the observed limit. The green and yellow bands around the expected limit indicate the &plusmn; 1&sigma; and &plusmn; 2&sigma; uncertainties, respectively. The expected signal production cross-sections as well as the associated uncertainty are indicated with the blue solid and dashed lines. The lower x-axis indicates the difference between the &chi;&#771;₂⁰ and &chi;&#771;₁⁰ masses for different values of m_1/2. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive this limit.

<b>Upper Limits 9</b> Expected and observed 95% CL cross-section upper limits as a function of the universal gaugino mass m_1/2 for the NUHM2 model. The gray numbers indicate the values of the observed limit. The green and yellow bands around the expected limit indicate the &plusmn; 1&sigma; and &plusmn; 2&sigma; uncertainties, respectively. The expected signal production cross-sections as well as the associated uncertainty are indicated with the blue solid and dashed lines. The lower x-axis indicates the difference between the &chi;&#771;₂⁰ and &chi;&#771;₁⁰ masses for different values of m_1/2. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive this limit.

<b>Upper Limits 10</b> Expected and observed 95% CL cross-section upper limits as a function of the universal gaugino mass m_1/2 for the NUHM2 model. The gray numbers indicate the values of the observed limit. The green and yellow bands around the expected limit indicate the &plusmn; 1&sigma; and &plusmn; 2&sigma; uncertainties, respectively. The expected signal production cross-sections as well as the associated uncertainty are indicated with the blue solid and dashed lines. The lower x-axis indicates the difference between the &chi;&#771;₂⁰ and &chi;&#771;₁⁰ masses for different values of m_1/2. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive this limit.

<b>Upper Limits 10</b> Expected and observed 95% CL cross-section upper limits as a function of the universal gaugino mass m_1/2 for the NUHM2 model. The gray numbers indicate the values of the observed limit. The green and yellow bands around the expected limit indicate the &plusmn; 1&sigma; and &plusmn; 2&sigma; uncertainties, respectively. The expected signal production cross-sections as well as the associated uncertainty are indicated with the blue solid and dashed lines. The lower x-axis indicates the difference between the &chi;&#771;₂⁰ and &chi;&#771;₁⁰ masses for different values of m_1/2. A fit of signals to the m_{&#8467;&#8467;} spectrum is used to derive this limit.

<b>Cutflow 2</b> Observed event yields and exclusion fit results with the signal strength parameter set to zero for the exclusive electroweakino and slepton signal regions. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. Uncertainties in the fitted background estimates combine statistical and systematic uncertainties.

<b>Cutflow 2</b> Observed event yields and exclusion fit results with the signal strength parameter set to zero for the exclusive electroweakino and slepton signal regions. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. Uncertainties in the fitted background estimates combine statistical and systematic uncertainties.

<b>Cutflow 3</b> Observed event yields and background-only fit results for the inclusive electroweakino and slepton signal regions. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. Uncertainties in the fitted background estimates combine statistical and systematic uncertainties.

<b>Cutflow 3</b> Observed event yields and background-only fit results for the inclusive electroweakino and slepton signal regions. Background processes containing fewer than two prompt leptons are categorized as `Fake/nonprompt'. The category `Others' contains rare backgrounds from triboson, Higgs boson, and the remaining top-quark production processes listed in Table 1. Uncertainties in the fitted background estimates combine statistical and systematic uncertainties.

<b>Exclusion 4</b> Nominal observed and expected CLs values for Higgsino signals.

<b>Exclusion 4</b> Nominal observed and expected CLs values for Higgsino signals.

<b>Exclusion 5</b> Nominal observed and expected CLs values for wino--bino signals.

<b>Exclusion 5</b> Nominal observed and expected CLs values for wino--bino signals.

<b>Exclusion 6</b> Nominal observed and expected CLs values for slepton signals.

<b>Exclusion 6</b> Nominal observed and expected CLs values for slepton signals.

<b>Upper Limits 11</b> Upper limits on observed (expected) Higgsino simplified model signal cross section &sigma;_{obs (exp)}⁹⁵ and signal strength &sigma;_{obs (exp)}⁹⁵ / &sigma;_theory.

<b>Upper Limits 11</b> Upper limits on observed (expected) Higgsino simplified model signal cross section &sigma;_{obs (exp)}⁹⁵ and signal strength &sigma;_{obs (exp)}⁹⁵ / &sigma;_theory.

<b>Upper Limits 12</b> Upper limits on observed (expected) wino--bino simplified model signal cross section &sigma;_{obs (exp)}⁹⁵ and signal strength &sigma;_{obs (exp)}⁹⁵ / &sigma;_theory.

<b>Upper Limits 12</b> Upper limits on observed (expected) wino--bino simplified model signal cross section &sigma;_{obs (exp)}⁹⁵ and signal strength &sigma;_{obs (exp)}⁹⁵ / &sigma;_theory.

<b>Upper Limits 13</b> Upper limits on observed (expected) slepton simplified model signal cross section &sigma;_{obs (exp)}⁹⁵ and signal strength &sigma;_{obs (exp)}⁹⁵ / &sigma;_theory.

<b>Upper Limits 13</b> Upper limits on observed (expected) slepton simplified model signal cross section &sigma;_{obs (exp)}⁹⁵ and signal strength &sigma;_{obs (exp)}⁹⁵ / &sigma;_theory.

<b>Cutflow 4</b> Event counts for Higgsino H and slepton &#8467; signals after sequential selections for the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} and SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Weighted events are normalised to mathcalL = 36.1 fb^-1 and the inclusive cross section &sigma;, while raw MC events are also shown. The generator filter with efficiency &epsilon;_filt applied to the Higgsino signal requires truth E_T^miss &gt; 50 GeV and at least 2 leptons with p_T &gt; 3 GeV, while only the E_T^miss &gt; 50 GeV requirement is applied to the slepton signal. The mathcalB refers to the branching ratio Z^(*) &rarr; &#8467;⁺&#8467;^- in the Higgsino processes. ``Lepton truth matching" requires that the selected leptons are consistent with being decay products of the SUSY process. ``Lepton author 16 veto" removes a class of electron candidates reconstructed with algorithms designed to identify photon conversions.

<b>Cutflow 4</b> Event counts for Higgsino H and slepton &#8467; signals after sequential selections for the inclusive SR&#8467;&#8467;-m_{&#8467;&#8467;} and SR&#8467;&#8467;-m_T2¹⁰⁰ regions. Weighted events are normalised to mathcalL = 36.1 fb^-1 and the inclusive cross section &sigma;, while raw MC events are also shown. The generator filter with efficiency &epsilon;_filt applied to the Higgsino signal requires truth E_T^miss &gt; 50 GeV and at least 2 leptons with p_T &gt; 3 GeV, while only the E_T^miss &gt; 50 GeV requirement is applied to the slepton signal. The mathcalB refers to the branching ratio Z^(*) &rarr; &#8467;⁺&#8467;^- in the Higgsino processes. ``Lepton truth matching" requires that the selected leptons are consistent with being decay products of the SUSY process. ``Lepton author 16 veto" removes a class of electron candidates reconstructed with algorithms designed to identify photon conversions.

<b>Cutflow 5</b> Event counts for the &chi;&#771;₂⁰&chi;&#771;₁⁺ process of the Higgsino m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) = (110, 100) GeV signal and sequentially with each addition requirement for selections common to all signal regions (SRs), followed by those optimised for Higgsinos and sleptons. ``Lepton truth matching" requires that the selected leptons are consistent with being decay products of the SUSY process. ``Lepton author 16 veto" removes a class of electron candidates reconstructed with algorithms designed to identify photon conversions. Weighted events are normalised to 36.1 fb^-1 and the raw Monte Carlo events are also displayed.

<b>Cutflow 5</b> Event counts for the &chi;&#771;₂⁰&chi;&#771;₁⁺ process of the Higgsino m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) = (110, 100) GeV signal and sequentially with each addition requirement for selections common to all signal regions (SRs), followed by those optimised for Higgsinos and sleptons. ``Lepton truth matching" requires that the selected leptons are consistent with being decay products of the SUSY process. ``Lepton author 16 veto" removes a class of electron candidates reconstructed with algorithms designed to identify photon conversions. Weighted events are normalised to 36.1 fb^-1 and the raw Monte Carlo events are also displayed.

<b>Cutflow 6</b> Event counts for the &chi;&#771;₂⁰&chi;&#771;₁^- process of the Higgsino m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) = (110, 100) GeV signal and sequentially with each addition requirement for selections common to all signal regions (SRs), followed by those optimised for Higgsinos and sleptons. ``Lepton truth matching" requires that the selected leptons are consistent with being decay products of the SUSY process. ``Lepton author 16 veto" removes a class of electron candidates reconstructed with algorithms designed to identify photon conversions. Weighted events are normalised to 36.1 fb^-1 and the raw Monte Carlo events are also displayed.

<b>Cutflow 6</b> Event counts for the &chi;&#771;₂⁰&chi;&#771;₁^- process of the Higgsino m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) = (110, 100) GeV signal and sequentially with each addition requirement for selections common to all signal regions (SRs), followed by those optimised for Higgsinos and sleptons. ``Lepton truth matching" requires that the selected leptons are consistent with being decay products of the SUSY process. ``Lepton author 16 veto" removes a class of electron candidates reconstructed with algorithms designed to identify photon conversions. Weighted events are normalised to 36.1 fb^-1 and the raw Monte Carlo events are also displayed.

<b>Cutflow 7</b> Event counts for the &chi;&#771;₂⁰&chi;&#771;₁⁰ process of the Higgsino m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) = (110, 100) GeV signal and sequentially with each addition requirement followed by those optimised for Higgsinos and sleptons. Weighted events are normalised to 36.1 fb^-1 and the raw Monte Carlo events are also displayed.

<b>Cutflow 7</b> Event counts for the &chi;&#771;₂⁰&chi;&#771;₁⁰ process of the Higgsino m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) = (110, 100) GeV signal and sequentially with each addition requirement followed by those optimised for Higgsinos and sleptons. Weighted events are normalised to 36.1 fb^-1 and the raw Monte Carlo events are also displayed.

<b>Cutflow 8</b> Event counts for the &chi;&#771;₁⁺&chi;&#771;₁^- process of the Higgsino m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) = (110, 100) GeV signal and sequentially with each addition requirement for selections common to all signal regions (SRs). ``Lepton truth matching" requires that the selected leptons are consistent with being decay products of the SUSY process. ``Lepton author 16 veto" removes a class of electron candidates reconstructed with algorithms designed to identify photon conversions. Weighted events are normalised to 36.1 fb^-1 and the raw Monte Carlo events are also displayed.

<b>Cutflow 8</b> Event counts for the &chi;&#771;₁⁺&chi;&#771;₁^- process of the Higgsino m(&chi;&#771;₂⁰, &chi;&#771;₁⁰) = (110, 100) GeV signal and sequentially with each addition requirement for selections common to all signal regions (SRs). ``Lepton truth matching" requires that the selected leptons are consistent with being decay products of the SUSY process. ``Lepton author 16 veto" removes a class of electron candidates reconstructed with algorithms designed to identify photon conversions. Weighted events are normalised to 36.1 fb^-1 and the raw Monte Carlo events are also displayed.

The extrapolated ($\tau > 30$ ps) average charged-particle muliplicity per unit of rapidity for ETARAP=0 as a function of the centre-of-mass energy.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of pseudorapidity for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of pseudorapidity for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of transverse momentum for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of transverse momentum for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distribution in proton-proton collisions at a centre-of mass energy of 13000 GeV for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distribution in proton-proton collisions at a centre-of mass energy of 13000 GeV for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Average transverse momentum in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Average transverse momentum in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Extrapolated ($\tau > 30$ ps) charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of pseudorapidity for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Extrapolated ($\tau > 30$ ps) charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of pseudorapidity for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Extrapolated ($\tau > 30$ ps) charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of transverse momentum for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Extrapolated ($\tau > 30$ ps) charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of transverse momentum for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Extrapolated ($\tau > 30$ ps) charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 13000 GeV for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Extrapolated ($\tau > 30$ ps) charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 13000 GeV for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Extrapolated ($\tau > 30$ ps) average transverse momentum in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Extrapolated ($\tau > 30$ ps) average transverse momentum in proton-proton collisions at a centre-of mass energy of 13000 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Probability of finding at least one TAR jet, where the p_T-leading TAR jet passes the m_Wcand and D₂^&beta;=1 requirements, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=2100 GeV, with the preselections applied.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=500 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=1000 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=1700 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=2100 GeV, with the preselections applied that do not pass the requirements of the merged category.

Observed exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected+1&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected-1&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected+2&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected-2&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=0.5 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=0.5 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=1 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=1 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=1.7 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=1.7 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=2.1 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=2.1 TeV, shown in dashed blue.

Data overlaid on SM background yields stacked in each SR and CR category after the fit to data ('Post-fit'). The yields in the SR are broken down into their contributions to the individual bins. The maximum-likelihood estimators are set to the conditional values of the CR-only fit, and propagated to SR and CRs.

Dominant sources of uncertainty for three dark Higgs scenarios after the fit to data. The uncertainties are quantified in terms of their contribution to the fitted signal uncertainty that is expressed relative to the theory prediction. Three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01 and m_&chi;=200 GeV are considered, which are indicated using the (m_Z', m_s) format in units of GeV in the table columns.

Cumulative efficiencies in the merged category for three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01, m_Z' = 1 TeV, and m_&chi;=200 GeV considering s&rarr;W(&ell;&nu;)W(qq) decays only.

Cumulative efficiencies in the resolved category for three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01, m_Z' = 1 TeV, and m_&chi;=200 GeV considering s&rarr;W(&ell;&nu;)W(qq) decays only.

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={300, 350, 400, 500, 750} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={1000, 1700} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={2100, 2500, 2900, 3300} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

The unfolded differential charge asymmetry as a function of the transverse momentum of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.

The unfolded differential charge asymmetry as a function of the longitudinal boost of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.

The unfolded inclusive leptonic asymmetry. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the invariant mass of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the transverse momentum of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the longitudinal boost of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

Individual 68% and 95% CL bounds on the relevant Wilson coefficients of the SM Effective Field Theory in units of $\text{TeV}^{-2}$. The bounds are derived from the $A_C^{t\bar{t}}$ inclusive measurement. The experimental uncertainties are accounted for, in the form of the complete covariance matrix that keeps track of correlations between bins for the differential measurement. The theory uncertainty from the NNLO QCD + NLO EW calculation is included by explicitly varying the renormalization and factorization scales, or the parton density functions, in the calculation and registering the variations in the intervals.

Individual 68% and 95% CL bounds on the relevant Wilson coefficients of the SM Effective Field Theory in units of $\text{TeV}^{-2}$. The bounds are derived from the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. The experimental uncertainties are accounted for, in the form of the complete covariance matrix that keeps track of correlations between bins for the differential measurement. The theory uncertainty from the NNLO QCD + NLO EW calculation is included by explicitly varying the renormalization and factorization scales, or the parton density functions, in the calculation and registering the variations in the intervals.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ inclusive measurement. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0,0.3]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.3,0.6]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.6,0.8]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.8,1]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ < 500 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [500,750] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [750,1000] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [1000,1500] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ > 1500 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ < 30 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ $\in$ [30,120] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ > 120 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ inclusive measurement. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0,0.3]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.3,0.6]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.6,0.8]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.8,1]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ < 200 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ $\in$ [200,300] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ $\in$ [300,400] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ > 400 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ < 20 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ $\in$ [20, 70] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ > 70 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ inclusive measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ inclusive measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Covariance matrix for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement.

Covariance matrix for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement.

Covariance matrix for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement.

Covariance matrix for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement.

Covariance matrix for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement.

Covariance matrix for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement.