Expected and observed 95\% \CL exclusion limits on the HH production cross section for the different channels and their combination for each benchmark model.

Expected and observed 95\% \CL exclusion limits on the HH production cross section for the bbVV channel and for each benchmark model.

Expected and observed 95\% \CL exclusion limits on the HH production cross section for the bbbb channel and for each benchmark model.

Expected and observed 95\% \CL exclusion limits on the HH production cross section for the bbtt channel and for each benchmark model.

Expected and observed 95\% \CL exclusion limits on the HH production cross section for the bbgg channel and for each benchmark model.

Expected and observed 95\% \CL exclusion limits on the HH production cross section for the combination of all channels and for each benchmark model.

Expected and observed 95\% \CL exclusion limits on the production of a narrow, spin two resonance (X) decaying into a pair of Higgs bosons.

Expected and observed exclusion limits (95% CL, in the asymptotic approximation) on the product of the production cross section and branching fraction into two photons for an additional SM-like Higgs boson, for the VBF plus VH processes, from the analysis of the 8 TeV data. The inner and outer bands indicate the regions containing the distribution of limits located within $pm 1 and 2 $sigma, respectively, of the expectation under the background-only hypothesis.

Expected and observed exclusion limits (95% CL, in the asymptotic approximation) on the product of the production cross section and branching fraction into two photons for an additional SM-like Higgs boson, for the ggH plus ttH processes, from the analysis of the 13 TeV data. The inner and outer bands indicate the regions containing the distribution of limits located within $pm 1 and 2 $sigma, respectively, of the expectation under the background-only hypothesis.

Expected and observed exclusion limits (95% CL, in the asymptotic approximation) on the product of the production cross section and branching fraction into two photons for an additional SM-like Higgs boson, for the VBF plus VH processes, from the analysis of the 13 (bottom) TeV data. The inner and outer bands indicate the regions containing the distribution of limits located within $pm 1 and 2 $sigma, respectively, of the expectation under the background-only hypothesis.

Expected and observed exclusion limits (95% CL, in the asymptotic approximation) on the product of the production cross section and branching fraction into two photons for an additional Higgs boson, relative to the expected SM-like value, from the analysis of the 8 and 13 TeV data. The inner and outer bands indicate the regions containing the distribution of limits located within $pm 1 and 2 $sigma , respectively, of the expectation under the background-only hypothesis.

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.

Covariance matrix of the normalised differential cross section at parton level as a function of $p_{T}^{t}$.

Measured absolute differential cross section at particle level as a function of $p_{T}^{t}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{t}$.

Measured normalised differential cross section at particle level as a function of $p_{T}^{t}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{t}$.

Measured absolute differential cross section at parton level as a function of $p_{T}^{\bar{t}}$.

Covariance matrix of the absolute differential cross section at parton level as a function of $p_{T}^{\bar{t}}$.

Measured normalised differential cross section at parton level as a function of $p_{T}^{\bar{t}}$.

Covariance matrix of the normalised differential cross section at parton level as a function of $p_{T}^{\bar{t}}$.

Measured absolute differential cross section at particle level as a function of $p_{T}^{\bar{t}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{\bar{t}}$.

Measured normalised differential cross section at particle level as a function of $p_{T}^{\bar{t}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{\bar{t}}$.

Measured absolute differential cross section at parton level as a function of $p_{T}^{t}$ (leading).

Covariance matrix of the absolute differential cross section at parton level as a function of $p_{T}^{t}$ (leading).

Measured normalised differential cross section at parton level as a function of $p_{T}^{t}$ (leading).

Covariance matrix of the normalised differential cross section at parton level as a function of $p_{T}^{t}$ (leading).

Measured absolute differential cross section at particle level as a function of $p_{T}^{t}$ (leading).

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{t}$ (leading).

Measured normalised differential cross section at particle level as a function of $p_{T}^{t}$ (leading).

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{t}$ (leading).

Measured absolute differential cross section at parton level as a function of $p_{T}^{t}$ (trailing).

Covariance matrix of the absolute differential cross section at parton level as a function of $p_{T}^{t}$ (trailing).

Measured normalised differential cross section at parton level as a function of $p_{T}^{t}$ (trailing).

Covariance matrix of the normalised differential cross section at parton level as a function of $p_{T}^{t}$ (trailing).

Measured absolute differential cross section at particle level as a function of $p_{T}^{t}$ (trailing).

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{t}$ (trailing).

Measured normalised differential cross section at particle level as a function of $p_{T}^{t}$ (trailing).

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{t}$ (trailing).

Measured absolute differential cross section at parton level as a function of $p_{T}^{t}$($t\bar{t}$ RF).

Covariance matrix of the absolute differential cross section at parton level as a function of $p_{T}^{t}$($t\bar{t}$ RF).

Measured normalised differential cross section at parton level as a function of $p_{T}^{t}$($t\bar{t}$ RF).

Covariance matrix of the normalised differential cross section at parton level as a function of $p_{T}^{t}$($t\bar{t}$ RF).

Measured absolute differential cross section at particle level as a function of $p_{T}^{t}$($t\bar{t}$ RF).

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{t}$($t\bar{t}$ RF).

Measured normalised differential cross section at particle level as a function of $p_{T}^{t}$($t\bar{t}$ RF).

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{t}$($t\bar{t}$ RF).

Measured absolute differential cross section at parton level as a function of $y_{t}$.

Covariance matrix of the absolute differential cross section at parton level as a function of $y_{t}$.

Measured normalised differential cross section at parton level as a function of $y_{t}$.

Covariance matrix of the normalised differential cross section at parton level as a function of $y_{t}$.

Measured absolute differential cross section at particle level as a function of $y_{t}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $y_{t}$.

Measured normalised differential cross section at particle level as a function of $y_{t}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $y_{t}$.

Measured absolute differential cross section at parton level as a function of $y_{\bar{t}}$.

Covariance matrix of the absolute differential cross section at parton level as a function of $y_{\bar{t}}$.

Measured normalised differential cross section at parton level as a function of $y_{\bar{t}}$.

Covariance matrix of the normalised differential cross section at parton level as a function of $y_{\bar{t}}$.

Measured absolute differential cross section at particle level as a function of $y_{\bar{t}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $y_{\bar{t}}$.

Measured normalised differential cross section at particle level as a function of $y_{\bar{t}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $y_{\bar{t}}$.

Measured absolute differential cross section at parton level as a function of $y_{t}$ (leading).

Covariance matrix of the absolute differential cross section at parton level as a function of $y_{t}$ (leading).

Measured normalised differential cross section at parton level as a function of $y_{t}$ (leading).

Covariance matrix of the normalised differential cross section at parton level as a function of $y_{t}$ (leading).

Measured absolute differential cross section at particle level as a function of $y_{t}$ (leading).

Covariance matrix of the absolute differential cross section at particle level as a function of $y_{t}$ (leading).

Measured normalised differential cross section at particle level as a function of $y_{t}$ (leading).

Covariance matrix of the normalised differential cross section at particle level as a function of $y_{t}$ (leading).

Measured absolute differential cross section at parton level as a function of $y_{t}$ (trailing).

Covariance matrix of the absolute differential cross section at parton level as a function of $y_{t}$ (trailing).

Measured normalised differential cross section at parton level as a function of $y_{t}$ (trailing).

Covariance matrix of the normalised differential cross section at parton level as a function of $y_{t}$ (trailing).

Measured absolute differential cross section at particle level as a function of $y_{t}$ (trailing).

Covariance matrix of the absolute differential cross section at particle level as a function of $y_{t}$ (trailing).

Measured normalised differential cross section at particle level as a function of $y_{t}$ (trailing).

Covariance matrix of the normalised differential cross section at particle level as a function of $y_{t}$ (trailing).

Measured absolute differential cross section at parton level as a function of $p_{T}^{t\bar{t}}$.

Covariance matrix of the absolute differential cross section at parton level as a function of $p_{T}^{t\bar{t}}$.

Measured normalised differential cross section at parton level as a function of $p_{T}^{t\bar{t}}$.

Covariance matrix of the normalised differential cross section at parton level as a function of $p_{T}^{t\bar{t}}$.

Measured absolute differential cross section at particle level as a function of $p_{T}^{t\bar{t}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{t\bar{t}}$.

Measured normalised differential cross section at particle level as a function of $p_{T}^{t\bar{t}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{t\bar{t}}$.

Measured absolute differential cross section at parton level as a function of $y_{t\bar{t}}$.

Covariance matrix of the absolute differential cross section at parton level as a function of $y_{t\bar{t}}$.

Measured normalised differential cross section at parton level as a function of $y_{t\bar{t}}$.

Covariance matrix of the normalised differential cross section at parton level as a function of $y_{t\bar{t}}$.

Measured absolute differential cross section at particle level as a function of $y_{t\bar{t}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $y_{t\bar{t}}$.

Measured normalised differential cross section at particle level as a function of $y_{t\bar{t}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $y_{t\bar{t}}$.

Measured absolute differential cross section at parton level as a function of $m_{t\bar{t}}$.

Covariance matrix of the absolute differential cross section at parton level as a function of $m_{t\bar{t}}$.

Measured normalised differential cross section at parton level as a function of $m_{t\bar{t}}$.

Covariance matrix of the normalised differential cross section at parton level as a function of $m_{t\bar{t}}$.

Measured absolute differential cross section at particle level as a function of $m_{t\bar{t}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $m_{t\bar{t}}$.

Measured normalised differential cross section at particle level as a function of $m_{t\bar{t}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $m_{t\bar{t}}$.

Measured absolute differential cross section at parton level as a function of $\Delta|y|(t,\bar{t})$.

Covariance matrix of the absolute differential cross section at parton level as a function of $\Delta|y|(t,\bar{t})$.

Measured normalised differential cross section at parton level as a function of $\Delta|y|(t,\bar{t})$.

Covariance matrix of the normalised differential cross section at parton level as a function of $\Delta|y|(t,\bar{t})$.

Measured absolute differential cross section at particle level as a function of $\Delta|y|(t,\bar{t})$.

Covariance matrix of the absolute differential cross section at particle level as a function of $\Delta|y|(t,\bar{t})$.

Measured normalised differential cross section at particle level as a function of $\Delta|y|(t,\bar{t})$.

Covariance matrix of the normalised differential cross section at particle level as a function of $\Delta|y|(t,\bar{t})$.

Measured absolute differential cross section at parton level as a function of $\Delta\phi(t,\bar{t})$.

Covariance matrix of the absolute differential cross section at parton level as a function of $\Delta\phi(t,\bar{t})$.

Measured normalised differential cross section at parton level as a function of $\Delta\phi(t,\bar{t})$.

Covariance matrix of the normalised differential cross section at parton level as a function of $\Delta\phi(t,\bar{t})$.

Measured absolute differential cross section at particle level as a function of $\Delta\phi(t,\bar{t})$.

Covariance matrix of the absolute differential cross section at particle level as a function of $\Delta\phi(t,\bar{t})$.

Measured normalised differential cross section at particle level as a function of $\Delta\phi(t,\bar{t})$.

Covariance matrix of the normalised differential cross section at particle level as a function of $\Delta\phi(t,\bar{t})$.

Measured absolute differential cross section at particle level as a function of $p_{T}^{l}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{l}$.

Measured normalised differential cross section at particle level as a function of $p_{T}^{l}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{l}$.

Measured absolute differential cross section at particle level as a function of $p_{T}^{\bar{l}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{\bar{l}}$.

Measured normalised differential cross section at particle level as a function of $p_{T}^{\bar{l}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{\bar{l}}$.

Measured absolute differential cross section at particle level as a function of $p_{T}^{l}$ (leading).

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{l}$ (leading).

Measured normalised differential cross section at particle level as a function of $p_{T}^{l}$ (leading).

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{l}$ (leading).

Measured absolute differential cross section at particle level as a function of $p_{T}^{l}$ (trailing).

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{l}$ (trailing).

Measured normalised differential cross section at particle level as a function of $p_{T}^{l}$ (trailing).

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{l}$ (trailing).

Measured absolute differential cross section at particle level as a function of $\eta_{l}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $\eta_{l}$.

Measured normalised differential cross section at particle level as a function of $\eta_{l}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $\eta_{l}$.

Measured absolute differential cross section at particle level as a function of $\eta_{\bar{l}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $\eta_{\bar{l}}$.

Measured normalised differential cross section at particle level as a function of $\eta_{\bar{l}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $\eta_{\bar{l}}$.

Measured absolute differential cross section at particle level as a function of $\eta_{l}$ (leading).

Covariance matrix of the absolute differential cross section at particle level as a function of $\eta_{l}$ (leading).

Measured normalised differential cross section at particle level as a function of $\eta_{l}$ (leading).

Covariance matrix of the normalised differential cross section at particle level as a function of $\eta_{l}$ (leading).

Measured absolute differential cross section at particle level as a function of $\eta_{l}$ (trailing).

Covariance matrix of the absolute differential cross section at particle level as a function of $\eta_{l}$ (trailing).

Measured normalised differential cross section at particle level as a function of $\eta_{l}$ (trailing).

Covariance matrix of the normalised differential cross section at particle level as a function of $\eta_{l}$ (trailing).

Measured absolute differential cross section at particle level as a function of $p_{T}^{l\bar{l}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{l\bar{l}}$.

Measured normalised differential cross section at particle level as a function of $p_{T}^{l\bar{l}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{l\bar{l}}$.

Measured absolute differential cross section at particle level as a function of $m_{l\bar{l}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $m_{l\bar{l}}$.

Measured normalised differential cross section at particle level as a function of $m_{l\bar{l}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $m_{l\bar{l}}$.

Measured absolute differential cross section at particle level as a function of $\Delta\phi(l,\bar{l})$.

Covariance matrix of the absolute differential cross section at particle level as a function of $\Delta\phi(l,\bar{l})$.

Measured normalised differential cross section at particle level as a function of $\Delta\phi(l,\bar{l})$.

Covariance matrix of the normalised differential cross section at particle level as a function of $\Delta\phi(l,\bar{l})$.

Measured absolute differential cross section at particle level as a function of $\Delta|\eta|(l,\bar{l})$.

Covariance matrix of the absolute differential cross section at particle level as a function of $\Delta|\eta|(l,\bar{l})$.

Measured normalised differential cross section at particle level as a function of $\Delta|\eta|(l,\bar{l})$.

Covariance matrix of the normalised differential cross section at particle level as a function of $\Delta|\eta|(l,\bar{l})$.

Measured absolute differential cross section at particle level as a function of $p_{T}^{b}$ (leading).

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{b}$ (leading).

Measured normalised differential cross section at particle level as a function of $p_{T}^{b}$ (leading).

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{b}$ (leading).

Measured absolute differential cross section at particle level as a function of $p_{T}^{b}$ (trailing).

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{b}$ (trailing).

Measured normalised differential cross section at particle level as a function of $p_{T}^{b}$ (trailing).

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{b}$ (trailing).

Measured absolute differential cross section at particle level as a function of $\eta_{b}$ (leading).

Covariance matrix of the absolute differential cross section at particle level as a function of $\eta_{b}$ (leading).

Measured normalised differential cross section at particle level as a function of $\eta_{b}$ (leading).

Covariance matrix of the normalised differential cross section at particle level as a function of $\eta_{b}$ (leading).

Measured absolute differential cross section at particle level as a function of $\eta_{b}$ (trailing).

Covariance matrix of the absolute differential cross section at particle level as a function of $\eta_{b}$ (trailing).

Measured normalised differential cross section at particle level as a function of $\eta_{b}$ (trailing).

Covariance matrix of the normalised differential cross section at particle level as a function of $\eta_{b}$ (trailing).

Measured absolute differential cross section at particle level as a function of $p_{T}^{b\bar{b}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $p_{T}^{b\bar{b}}$.

Measured normalised differential cross section at particle level as a function of $p_{T}^{b\bar{b}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $p_{T}^{b\bar{b}}$.

Measured absolute differential cross section at particle level as a function of $m_{b\bar{b}}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $m_{b\bar{b}}$.

Measured normalised differential cross section at particle level as a function of $m_{b\bar{b}}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $m_{b\bar{b}}$.

Measured absolute differential cross section at particle level as a function of $N_{jets}$.

Covariance matrix of the absolute differential cross section at particle level as a function of $N_{jets}$.

Measured normalised differential cross section at particle level as a function of $N_{jets}$.

Covariance matrix of the normalised differential cross section at particle level as a function of $N_{jets}$.

Differential cross section at W= 1.720 GeV

Differential cross section at W= 1.750 GeV

Differential cross section at W= 1.775 GeV

Differential cross section at W= 1.795 GeV

Differential cross section at W= 1.815 GeV

Differential cross section at W= 1.835 GeV

Differential cross section at W= 1.855 GeV

Differential cross section at W= 1.630 GeV

Differential cross section at W= 1.660 GeV

Differential cross section at W= 1.690 GeV

Differential cross section at W= 1.720 GeV

Differential cross section at W= 1.750 GeV

Differential cross section at W= 1.775 GeV

Differential cross section at W= 1.795 GeV

Differential cross section at W= 1.815 GeV

Differential cross section at W= 1.835 GeV

Differential cross section at W= 1.855 GeV

Differential cross section at W= 1.690 GeV

Differential cross section at W= 1.720 GeV

Differential cross section at W= 1.750 GeV

Differential cross section at W= 1.775 GeV

Differential cross section at W= 1.795 GeV

Differential cross section at W= 1.815 GeV

Differential cross section at W= 1.835 GeV

Differential cross section at W= 1.855 GeV

Measured misidentification probability distribution as a function of track multiplicity for the EMJ-1 criteria group defined in Table 2. The red up-pointing triangles are for b jets while the blue down-pointing triangles are for light-flavor jets. The horizontal lines on the data points indicate the variable bin width.

The $H_\mathrm{T}$ distribution for the observed data events (black points) and the predicted background estimation (blue) for selection set 8 (SM QCD-enhanced), requiring at least two jets tagged by loose emerging jet criteria.

The number of associated tracks distribution for the observed data events (black points) and the predicted background estimation (blue) for selection set 8 (SM QCD-enhanced), requiring at least two jets tagged by loose emerging jet criteria.

The $H_\mathrm{T}$ distribution for the observed data events (black points) and the predicted background estimation (blue) for selection set 9 (SM QCD-enhanced), requiring at least one jet tagged by loose emerging jet criteria and large $p_\mathrm{T}^\mathrm{miss}$.

The number of associated tracks distribution for the observed data events (black points) and the predicted background estimation (blue) for selection set 9 (SM QCD-enhanced), requiring at least one jet tagged by loose emerging jet criteria and large $p_\mathrm{T}^\mathrm{miss}$.

Upper limits at 95% CL on the signal cross section for models with dark pion mass $(m_{\pi_\mathrm{DK}})$ of 5 GeV in the proper decay length $(c\tau_{\pi_\mathrm{DK}})$ versus dark mediator mass $(m_{\mathrm{X_{DK}}})$ plane.

Dark mediator mass exclusion limits at 95% CL derived from theoretical cross sections for models with dark pion mass $(m_{\pi_\mathrm{DK}})$ of 5 GeV in the proper decay length $(c\tau_{\pi_\mathrm{DK}})$ versus dark mediator mass $(m_{\mathrm{X_{DK}}})$ plane.

Measured differential cross sections for the $pp \rightarrow \nu\bar{\nu}\gamma$ process as a function of $p_{T}^{\nu\bar{\nu}}$ in the inclusive $N_{jets} \geq 0$ extended fiducial region defined in the paper.

Measured differential cross sections for the $pp \rightarrow \nu\bar{\nu}\gamma$ process as a function of $p_{T}^{\nu\bar{\nu}}$ in the exclusive $N_{jets} = 0$ extended fiducial region defined in the paper.

Measured differential cross sections as a function of $N_{jets}$ in the extended fiducial region defined in the paper.

Observed and expected one dimensional 95% C.L. limits on vertex function parameters for the $ZZ\gamma$ and $Z\gamma\gamma$ vertices.

Observed and expected one dimensional 95% C.L. limits on EFT parameters for the $ZZ\gamma$ and $Z\gamma\gamma$ vertices.

Detector-level photon $\phi$ distribution

Detector-level dipoton invariant mass distribution

Detector-level diphoton rapidity distribution

Detector-level dipoton p$_{T}$ distribution

Observed exclusion limits at 95% CL in the ALP-photon coupling g$_{a \gamma}$ versus ALP mass $m_{a}$ plane.

Distributions of $\min[M(\ell,\mathrm{j})]$ in the W+jets control region, for 0 W-tagged jet category. Example signal distributions are also shown. The background distributions correspond to background-only fit to data while signal distributions are before the fit to data. Electron and muon event samples are combined. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{j})]$ in the W+jets control region, for 1 or more W-tagged jet category. Example signal distributions are also shown. The background distributions correspond to background-only fit to data while signal distributions are before the fit to data. Electron and muon event samples are combined. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 0 t-tagged jets, 0 W-tagged jets, and 1 b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 0 t-tagged jets, 0 W-tagged jets, and 2 or more b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 0 t-tagged jets, 1 or more W-tagged jets, and 1 b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 0 t-tagged jets, 1 or more W-tagged jets, and 2 or more b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 1 or more t-tagged jets, 0 W-tagged jets, and 1 b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 1 or more t-tagged jets, 0 W-tagged jets, and 2 or more b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 1 or more t-tagged jets, 1 or more W-tagged jets, and 1 b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 1 or more t-tagged jets, 1 or more W-tagged jets, and 2 or more b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Expected and observed limits at 95% CL for an LH $X_{5/3}$ after combining all categories for the same-sign dilepton final state. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an RH $X_{5/3}$ after combining all categories for the same-sign dilepton final state. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an LH $X_{5/3}$ after combining all categories for the single-lepton final state. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an RH $X_{5/3}$ after combining all categories for the single-lepton final state. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an LH $X_{5/3}$ after combining the same-sign dilepton and single-lepton final states. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an RH $X_{5/3}$ after combining the same-sign dilepton and single-lepton final states. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected 95% CL exclusion contour in the $m_{W_R}–m_{N_R}$ plane for the Majorana $N_R$ neutrino $\mu\mu$ channel.

Observed 95% CL exclusion contour in the $m_{W_R}–m_{N_R}$ plane for the Majorana $N_R$ neutrino $\mu\mu$ channel.

Observed and expected 95% CL exclusion, for the tested signal mass hypotheses in the $m_{W_R}–m_{N_R}$ plane, for the Majorana $N_R$ neutrino $\mu\mu$ channel.

Expected 95% CL exclusion contour in the $m_{W_R}–m_{N_R}$ plane for the Dirac $N_R$ neutrino $ee$ channel.

Observed 95% CL exclusion contour in the $m_{W_R}–m_{N_R}$ plane for the Dirac $N_R$ neutrino $ee$ channel.

Observed and expected 95% CL exclusion, for the tested signal mass hypotheses in the $m_{W_R}–m_{N_R}$ plane, for the Dirac $N_R$ neutrino $ee$ channel.

Expected 95% CL exclusion contour in the $m_{W_R}–m_{N_R}$ plane for the Dirac $N_R$ neutrino $\mu\mu$ channel.

Observed 95% CL exclusion contour in the $m_{W_R}–m_{N_R}$ plane for the Dirac $N_R$ neutrino $\mu\mu$ channel.

Observed and expected 95% CL exclusion, for the tested signal mass hypotheses in the $m_{W_R}–m_{N_R}$ plane, for the Dirac $N_R$ neutrino $\mu\mu$ channel.

Observed 95% CL upper limit on cross-section times branching ratio to the $ee$ final state for the Keung-Senjanovic process in the $m_{W_R}–m_{N_R}$ plane for the Majorana $N_R$ neutrino $ee$ channel.

Observed 95% CL upper limit on cross-section times branching ratio to the $\mu\mu$ final state for the Keung-Senjanovic process in the $m_{W_R}–m_{N_R}$ plane for the Majorana $N_R$ neutrino $\mu\mu$ channel.

Observed 95% CL upper limit on cross-section times branching ratio to the $\mu\mu$ final state for the Keung-Senjanovic process in the $m_{W_R}–m_{N_R}$ plane for the Dirac $N_R$ neutrino $ee$ channel.

Observed 95% CL upper limit on cross-section times branching ratio to the $\mu\mu$ final state for the Keung-Senjanovic process in the $m_{W_R}–m_{N_R}$ plane for the Dirac $N_R$ neutrino $\mu\mu$ channel.

Efficiencies times acceptance for signal region selection as a function of the signal $W_R$ and $N_R$ masses for the SS $e^{\pm}e^{\pm}$ channel.

Efficiencies times acceptance for signal region selection as a function of the signal $W_R$ and $N_R$ masses for the SS $\mu^{\pm}\mu^{\pm}$ channel.

Efficiencies times acceptance for signal region selection as a function of the signal $W_R$ and $N_R$ masses for the OS $e^{\pm}e^{\mp}$ channel.

Efficiencies times acceptance for signal region selection as a function of the signal $W_R$ and $N_R$ masses for the OS $\mu^{\pm}\mu^{\mp}$ channel.