Example description

Example description

Example description

Observed profiled likelihood on $f_{\Lambda1}$ using Approach 1. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{a3}$ using Approach 1. The signal strength modifiers are treated as free parameters.

Observed profiled likelihood on $f_{a3}$ using Approach 1. The signal strength modifiers are treated as free parameters.

Expected profiled likelihood on $f_{\Lambda1}^{Z\gamma}$ using Approach 1. The signal strength modifiers are treated as free parameters.

Observed profiled likelihood on $f_{\Lambda1}^{Z\gamma}$ using Approach 1. The signal strength modifiers are treated as free parameters.

Expected profiled likelihood on $f_{a2}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Observed profiled likelihood on $f_{a2}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{\Lambda1}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Observed profiled likelihood on $f_{\Lambda1}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{a3}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters.

Observed profiled likelihood on $f_{a3}$ using Approach 2. The other two anomalous coupling cross section fractions are fixed to zero. The signal strength modifiers are treated as free parameters.

Expected profiled likelihood on $f_{a2}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Observed profiled likelihood on $f_{a2}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{\Lambda1}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Observed profiled likelihood on $f_{\Lambda1}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters. Axis scales are varied to improve the visibility of important features.

Expected profiled likelihood on $f_{a3}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters.

Observed profiled likelihood on $f_{a3}$ using Approach 2. The other two anomalous coupling cross section fractions are left floating in the fit. The signal strength modifiers are treated as free parameters.

Expected profiled likelihood on the $\delta c_{Z}$ coupling of the SMEFT Higgs basis.

Observed profiled likelihood on the $\delta c_{Z}$ coupling of the SMEFT Higgs basis.

Expected profiled likelihood on the $c_{Z\Box}$ coupling of the SMEFT Higgs basis.

Observed profiled likelihood on the $c_{Z\Box}$ coupling of the SMEFT Higgs basis.

Expected profiled likelihood on the $c_{ZZ}$ coupling of the SMEFT Higgs basis.

Observed profiled likelihood on the $c_{ZZ}$ coupling of the SMEFT Higgs basis.

Expected profiled likelihood on the $\tilde{c}_{ZZ}$ coupling of the SMEFT Higgs basis.

Observed profiled likelihood on the $\tilde{c}_{ZZ}$ coupling of the SMEFT Higgs basis.

Expected profiled likelihood on $f_{a3}^{ggH}$. The signal strength modifiers and the CP-odd HVV anomalous coupling cross section fraction are treated as free parameters.

Observed profiled likelihood on $f_{a3}^{ggH}$. The signal strength modifiers and the CP-odd HVV anomalous coupling cross section fraction are treated as free parameters.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHbH channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bZtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHbH channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bZtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 3-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 3-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 4-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 4-jet bZbZ channel.

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 0% $\mathcal{B}(B \to bH)$, 100% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 25% $\mathcal{B}(B \to bH)$, 25% $\mathcal{B}(B \to bH)$, and 50% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 50% $\mathcal{B}(B \to bH)$, 50% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 100% $\mathcal{B}(B \to bH)$, 0% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

Median expected exclusion limits on the VLQ mass at 95% CL as a function of the branching fractions $\mathcal{B}(B \to bH)$ and $\mathcal{B}(B \to tW)$, with $\mathcal{B}(B \to tW) = 1 - \mathcal{B}(B \to bH) - \mathcal{B}(B \to bZ)$. The grey area corresponds to the region where the exclusion limit is less than 1000 GeV.

Median observed exclusion limits on the VLQ mass at 95% CL as a function of the branching fractions $\mathcal{B}(B \to bH)$ and $\mathcal{B}(B \to tW)$, with $\mathcal{B}(B \to tW) = 1 - \mathcal{B}(B \to bH) - \mathcal{B}(B \to bZ)$. The grey area corresponds to the region where the exclusion limit is less than 1000 GeV.

The geometric acceptance multiplied by the efficiency of the $\it{p}_{T}^\text{miss} >$ 200 GeV selection as a function of the proper decay length c$\tau$ for a scalar particle S with a mass of 40 GeV. The acceptance region for DT is defined by requiring the LLP to decay in the region with |z| < 661 cm and 380 cm < r < 736 cm. The acceptance region for CSC is defined by requiring the LLP decay in the region with $|\eta| < 2.4$, r < 695.5 cm, and 661 cm < |z| < 1100 cm or in the region with $|\eta| < 2.4$, r < 270 cm, and 500 cm < |z| < 661 cm. Single CSC cluster requires exactly one LLP to decay in CSC; Single DT cluster requires exactly one LLP to decay in DT; Double cluster requires both LLP to decay in CSC or DT. The denominator in this plot includes all generated events. The nominator includes events that pass the acceptance requirements above and $\it{p}_{T}^\text{miss} >$ 200 GeV.

Distributions of the cluster time for signal, where S decaying to $d\bar{d}$ for a proper decay length c$\tau$ of 1 m and mass of 40 GeV, and for a background-enriched sample in data selected by inverting the $N_\text{hits}$ requirement.

The distributions of $N_\text{hits}$ for single CSC clusters are shown for signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m).

The distributions of $N_\text{hits}$ for single CSC clusters are shown for compared to the OOT background ($t_\text{clusters} < 12.5$ ns). The OOT background is representative of the overall background shape, because the background passing all the selections described above is dominated by pileup and underlying events.

The distributions of $\Delta\phi(\it{p}_{T}^\text{miss} \text{,cluster)}$ for single CSC clusters are shown for signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), compared to the shape of background in a selection in which the cluster is not matched to any RPC hit and passes all other selections. The background is dominated by clusters from noise and low-pT particles.

The distributions of $N_\text{hits}$ for single CSC clusters are shown for signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), compared to the shape of background in a selection in which the cluster is not matched to any RPC hit and passes all other selections. The background is dominated by clusters from noise and low-pT particles.

The distributions of $\Delta\phi(\it{p}_{T}^\text{miss} \text{,cluster)}$ for single CSC clusters are shown for signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), compared to the shape of background in a selection in which the cluster is not matched to any RPC hit and passes all other selections. The background is dominated by clusters from noise and low-pT particles.

The signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), background, and data distributions of $N_\text{clusters}$ passing the $N_\text{hits}$ selection in the search region for CSC-CSC category.

The signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), background, and data distributions of $N_\text{clusters}$ passing the $N_\text{hits}$ selection in the search region for DT-DT category.

The signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), background, and data distributions of $N_\text{clusters}$ passing the $N_\text{hits}$ selection in the search region for DT-CSC category.

The signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), background, and data distributions of $N_\text{hits}$ in the search region of the single-CSC cluster category are shown. The $N_\text{hits}$ distribution includes only events in bins A and D. The right-hand bin in the Nhits distribution includes overflow events.

The signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), background, and data distributions of $\Delta\phi(\it{p}_{T}^\text{miss} \text{,cluster)}$ in the search region of the single-CSC cluster category are shown. The $\Delta\phi(\it{p}_{T}^\text{miss} \text{,cluster)}$ distribution includes only events in bins A and B.

The signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), background, and data distributions of $N_\text{hits}$ in the search region of the single-DT cluster category are shown. The $N_\text{hits}$ distribution includes only events in bins A and D. The right-hand bin in the Nhits distribution includes overflow events.

The signal (assuming B(H $\rightarrow$ SS) = 1%, S $\rightarrow d\bar{d}$, and c$\tau$ = 1 m), background, and data distributions of $\Delta\phi(\it{p}_{T}^\text{miss} \text{,cluster)}$ in the search region of the single-DT cluster category are shown. The $\Delta\phi(\it{p}_{T}^\text{miss} \text{,cluster)}$ distribution includes only events in bins A and B.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 3 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 0.4 GeV mass and $ S \rightarrow \pi^{0} \pi^{0}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 1 GeV mass and $ S \rightarrow \pi^{0} \pi^{0}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 0.4 GeV mass and $ S \rightarrow \pi^{+} \pi^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 1 GeV mass and $ S \rightarrow \pi^{+} \pi^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 1.5 GeV mass and $ S \rightarrow K^{+}K^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 1.5 GeV mass and $ S \rightarrow K^{0}K^{0}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 0.4 GeV mass and $ S \rightarrow \gamma\gamma$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 0.4 GeV mass and $ S \rightarrow \e^{+} \e^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) as functions of mass and c$\tau$ assuming S inherits all couplings from the Higgs boson evaluated at the LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for vector portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 2 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for vector portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for vector portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for vector portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for vector portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for gluon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 2 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 2 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 2 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for photon portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 4 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 4 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 4 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for higgs portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 1)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (2.5, 2.5)$ and 20 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 5 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 10 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 15 GeV LLP mass.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow \Psi\Psi$) for darkphoton portal, assuming $(\xi_{\omega}$, $\xi_{\Lambda}) = (1, 1)$ and 20 GeV LLP mass.

Correlation matrix for the differential branching fraction extraction between different $q^2$ bins in the simultaneous fit.

The product of acceptance and efficiency ($A\epsilon$) of the $\mathcal{B}(\mathrm{B}^+\to\mathrm{K}^+\mu^+\mu^-)$ channel, as a function of the muon pair $q^2$, as measured in simulated signal events, after all the corrections applied. In the figure, regions corresponding to resonances are displayed with red markers.

Same as Table 5, but for the bins containing the J$\psi$ and $\psi$(2S) resonances.

Integrated branching fraction $\mathcal{B}(\mathrm{B}^\pm \to \mathrm{K}^\pm\mu^+\mu^-)$ in the low-$q^2$ region.

Ratio of branching fractions $\mathcal{B}(\text{B}^\pm \to \text{K}^\pm\mu^+\mu^-)$ to $\mathcal{B}(\text{B}^\pm \to \text{K}^\pm\text{e}^+\text{e}^-)$, determined from the measured double ratio $R$(K) of these decays to the respective branching fractions of the decay chains $\text{B}^\pm\to\text{J/}\psi\text{K}^\pm$ with $\text{J/}\psi\to\mu^+\mu^-$ and $\text{e}^+\text{e}^-$.

Negative log likelihood function from the fit profiled as a function of the inverse ratio $R(\mathrm{K})^{-1}$.

Post-fit distributon of BDT discriminants for $\rho_{tc}=1.0$ with $m_A$ = 350 GeV interfered with H.($m_A - m_H$ = 50 GeV)

Observed and expected 95% CL upper limit on the signal strength as a function of $m_A$ for the g2HDM signal model using different coupling assumptions: $\rho_{tu}$ = 0.1, 0.4, 1.0.

Observed and expected 95% CL upper limit on the signal strength as a function of $m_A$ for the g2HDM signal model using different coupling assumptions: $\rho_{tc}$ = 0.1, 0.4, 1.0.

Observed and expected 95% CL upper limit on the signal strength as a function of $m_A$ for the g2HDM signal model using different coupling assumptions: $\rho_{tu}$ = 0.1, 0.4, 1.0 with A–H interference assuming $m_A$ − $m_H$ = 50 GeV.

Observed and expected 95% CL upper limit on the signal strength as a function of $m_A$ for the g2HDM signal model using different coupling assumptions: $\rho_{tc}$ = 0.1, 0.4, 1.0 with A–H interference assuming $m_A$ − $m_H$ = 50 GeV.

Observed 95% CL upper limit on the signal strength as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model.

Expected exclusion as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model

Observed exclusion as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model

Observed 95% CL upper limit on the signal strength as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model.

Expected exclusion as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model

Observed exclusion as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model

Observed 95% CL upper limit on the signal strength as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV.

Expected exclusion as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV

Observed exclusion as a function of $m_A$ and $\rho_{tu}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV

Observed 95% CL upper limit on the signal strength as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV.

Expected exclusion as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV

Observed exclusion as a function of $m_A$ and $\rho_{tc}$ for the g2HDM signal model with A–H interference assuming $m_A$ − $m_H$ = 50 GeV

The $m_{e\mu}$ distribution of the observed data is shown, with the $S+B$ fit at $m_{X}=146\mathrm{GeV}$ in red solid line, and the background component of the fit in red dotted line. Events and fit in each category are weighted by $S/(S+B)$. The one and two standard deviation uncertainty bands of the background component are shown in green and yellow. The lower panel shows the residuals after subtracting the background component of the fit from data.

Observed (expected) 95% confidence level upper limits on $\sigma(p p \to X(146) \to e \mu)$ for each individual analysis category (as shown in the left axis label) and for the combination of all analysis categories assuming the relative SM-like production cross sections of the ggH and VBF production modes.

The $m_{e\mu}$ distribution of the observed data in the category ggH cat 0 is shown, with the $S+B$ fit at $m_{X}=146\mathrm{GeV}$ in solid red line, and the background component of the fit in dotted red line. The green and yellow bands represent the one and two standard deviations of the background component. The lower panel shows the residuals after subtracting the background component of the fit from data.

The $m_{e\mu}$ distribution of the observed data in the category ggH cat 1 is shown, with the $S+B$ fit at $m_{X}=146\mathrm{GeV}$ in solid red line, and the background component of the fit in dotted red line. The green and yellow bands represent the one and two standard deviations of the background component. The lower panel shows the residuals after subtracting the background component of the fit from data.

The $m_{e\mu}$ distribution of the observed data in the category ggH cat 2 is shown, with the $S+B$ fit at $m_{X}=146\mathrm{GeV}$ in solid red line, and the background component of the fit in dotted red line. The green and yellow bands represent the one and two standard deviations of the background component. The lower panel shows the residuals after subtracting the background component of the fit from data.

The $m_{e\mu}$ distribution of the observed data in the category ggH cat 3 is shown, with the $S+B$ fit at $m_{X}=146\mathrm{GeV}$ in solid red line, and the background component of the fit in dotted red line. The green and yellow bands represent the one and two standard deviations of the background component. The lower panel shows the residuals after subtracting the background component of the fit from data.

The $m_{e\mu}$ distribution of the observed data in the category VBF cat 0 is shown, with the $S+B$ fit at $m_{X}=146\mathrm{GeV}$ in solid red line, and the background component of the fit in dotted red line. The green and yellow bands represent the one and two standard deviations of the background component. The lower panel shows the residuals after subtracting the background component of the fit from data.

The $m_{e\mu}$ distribution of the observed data in the category VBF cat 1 is shown, with the $S+B$ fit at $m_{X}=146\mathrm{GeV}$ in solid red line, and the background component of the fit in dotted red line. The green and yellow bands represent the one and two standard deviations of the background component. The lower panel shows the residuals after subtracting the background component of the fit from data.

Best fit of $\sigma(p p \to X(146) \to e \mu)$ for each analysis category (black point) compared to the best fit for the combination of all analysis categories (blue line). The one standard deviation uncertainty of the per-category best fit is shown in red line and the one standard deviation uncertainty of the combined best fit is shown in green band.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.0.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.1.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.2.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.3.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.4.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.5.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.6.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.7.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.8.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 0.9.

The observed and expected 95% CL upper limits on $\sigma_{ggH,f_{VBF}}(p p \to X \to e \mu)$ and $\sigma_{VBF,f_{VBF}}(p p \to X \to e \mu)$ with $f_{VBF}$ = 1.0.

Observed and expected 95% CL upper limits on ${{(\sigma_{\text{H}}/\sigma_{\text{H}}^{\mathrm{SM}}) \times {{\mathcal{B}(\text{H} \to \text{inv})}}}}$ for the combined VBF, ttH, VH and ggH channels using all available CMS data for 2016--2018, Run1, 2015, and their combination, assuming a SM Higgs boson with a mass of 125 GeV.

Observed and expected 95% CL upper limits on ${{(\sigma_{\text{H}}/\sigma_{\text{H}}^{\mathrm{SM}}) \times {{\mathcal{B}(\text{H} \to \text{inv})}}}}$ for the VBF, ttH(lep), ttH(had), V(qq)H, Z(ll)H, Z(bb)H and ggH channels using all available CMS data, and their combination, assuming a SM Higgs boson with a mass of 125 GeV.

The 90% CL upper limits on the spin-independent DM-nucleon scattering cross section in Higgs-portal models, assuming a scalar, fermionic or vectorial DM candidate (with a UV-complete vector model), compared to direct detection experimets CRESST-III, DarkSide-50, LUX_ZEPLIN and PandaX-4T (2022 results).

Postfit event yields in each category and hadronic recoil bin of the ttH category SR for the different background and signal processes summed across 2016, 2017 and 2018 samples. The background and signal yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR or CR-only. The Postfit signal contributions for a Higgs boson, produced in ttH, VH (split into WH and ZH), ggH and VBF, decaying to invisible particles with a branching fraction of $\mathcal{B}(\text{H} \to \text{inv}) = 1$, and the observed event yields are reported.

Postfit event yields in each category and hadronic recoil bin of the VH category SR for the different background and signal processes summed across 2016, 2017 and 2018 samples. The background and signal yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR or CR-only. The Postfit signal contributions for a Higgs boson, produced in ttH, VH (split into WH and ZH), ggH and VBF, decaying to invisible particles with a branching fraction of $\mathcal{B}(\text{H} \to \text{inv}) = 1$, and the observed event yields are reported.

Postfit event yields in each category and $p\mathrm{_{T}^{miss}}$ bin of the ttH category e$+$jets CR for the different background processes summed across 2016, 2017 and 2018 samples. The background yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR or CR-only.

Postfit event yields in each category and $p\mathrm{_{T}^{miss}}$ bin of the VH category e$+$jets CR for the different background processes summed across 2016, 2017 and 2018 samples. The background yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR or CR-only.

Postfit event yields in each category and $p\mathrm{_{T}^{miss}}$ bin of the ttH category $\mu+$jets CR for the different background processes summed across 2016, 2017 and 2018 samples. The background yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR or CR-only.

Postfit event yields in each category and $p\mathrm{_{T}^{miss}}$ bin of the VH category $\mu+$jets CR for the different background processes summed across 2016, 2017 and 2018 samples. The background yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR or CR-only.

Postfit event yields in each category and $p\mathrm{_{T}^{miss}}$ bin of the ttH category ee$+$jets, $\mu\mu+$jets, and $\ell\ell+$jets CRs for the different background processes summed across 2016, 2017 and 2018 samples. The background yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR or CR-only.

Postfit event yields in each category and $p\mathrm{_{T}^{miss}}$ bin of the VH category ee$+$jets, $\mu\mu+$jets, and $\gamma+$jets CRs for the different background processes summed across 2016, 2017 and 2018 samples. The background yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR or CR-only.

Electron $p_{T}$ distribution using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

$p_{T}^{miss}$ distribution in the electron channel using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

Muon $p_T$ distribution using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

$p_T^{miss}$ distribution in the muon channel using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

$M_T$ distribution for the E+INVISIBLE system using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

$M_T$ distribution for the MU+INVISIBLE system using 2016-2018 data sets. The complete set of selection criteria were applied. Two examples of SSM WPRIME boson signal samples with masses of 3.8 and 5.6 TeV are shown. The lower panel shows the ratio of data to SM prediction, and the shaded band represents the systematic uncertainties.

Theoretical prediction for the production and decay cross-section of SSM WPRIME bosons (W --> LEPTON + NU) as a function of the WPRIME mass, for the range 200-6400 GeV and inclusive in phase-space (no cuts). The uncertainty shown covers PDF and ALPHAS uncertainties. These theoretical predictions are obtained with PYTHIA (LO) and corrected to NNLO in QCD using FEWZ program.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the product of WPRIME cross-section production and BF(WPRIME --> LEPTON + NU) for an SSM WPRIME boson, as a function of the boson mass, for the electron decay channel. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits. The theoretical predictions for the SSM at NNLO precision in QCD are shown with narrow gray bands corresponding to the PDF and ALPHAS uncertainties.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the product of WPRIME cross-section production and BF(WPRIME --> LEPTON + NU) for an SSM WPRIME boson, as a function of the boson mass, for the muon decay channel. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits. The theoretical predictions for the SSM at NNLO precision in QCD are shown with narrow gray bands corresponding to the PDF and ALPHAS uncertainties.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the product of WPRIME cross-section production and BF(WPRIME --> LEPTON + NU) for an SSM WPRIME boson, as a function of the boson mass, for the combined electron and muon decay channels. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits. The theoretical predictions for the SSM at NNLO precision in QCD are shown with narrow gray bands corresponding to the PDF and ALPHAS uncertainties.

The 95% CL observed (solid line) and expected (dashed line) model independent cross section upper limits as function of the $MT_{min}$ threshold, for the electron decay channel. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits.

The 95% CL observed (solid line) and expected (dashed line) model independent cross section upper limits as function of the $MT_{min}$ threshold, for the muon decay channel. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits.

The 95% CL observed (solid line) and expected (dashed line) model independent cross section upper limits as function of the $MT_{min}$ threshold, for the combined electron and muon decay channels. The shaded bands represent the one (green) and two (yellow) standard deviation uncertainty bands for the expected limits.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the coupling strength ratio, gWprime/gW as a function of the mass of the WPRIME boson, for the electron channel. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The area above the limit curve is excluded. The dotted line represents the case of SSM coupling, gWprime, being equal to gW, the SM coupling.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the coupling strength ratio, gWprime/gW, as a function of the mass of the WPRIME boson, for the muon channel. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The area above the limit curve is excluded. The dotted line represents the case of SSM coupling, gWprime , being equal to gW, the SM coupling.

Exclusion limits in the 2D plane (1/R, $\mu$) for the split-UED interpretation for the n = 2 case, for the electron channel for the 2016-2018 data sets. R is the radius of the additional spatial dimension and $\mu$ the bulk mass for the fermion field in five dimensions. The expected limit is depicted as a black dashed line. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The experimentally excluded region is the entire area to the left of the solid black line.

Exclusion limits in the 2D plane (1/R, $\mu$) for the split-UED interpretation for the n = 2 case, for the muon channel for the 2016-2018 data sets. R is the radius of the additional spatial dimension and $\mu$ the bulk mass for the fermion field in five dimensions. The expected limit is depicted as a black dashed line. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The experimentally excluded region is the entire area to the left of the solid black line.

Exclusion limits in the 2D plane (1/R, $\mu$) for the split-UED interpretation for the n = 2 case, for the combined electron and muon channels for the 2016-2018 data sets. R is the radius of the additional spatial dimension and $\mu$ the bulk mass for the fermion field in five dimensions. The expected limit is depicted as a black dashed line. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The experimentally excluded region is the entire area to the left of the solid black line.

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the ${\lambda}_{231}$ coupling in the RPV SUSY model with a STAU mediator, as a function of the mass of STAU, for the electron channel and for the production coupling, ${\lambda}^{'}_{3ij}$=0.5 . The couplings ${\lambda}^{'}_{3ij}$, ${\lambda}_{231}$, and ${\lambda}_{132}$ are defined in Fig. 1. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The area above the limit curve is excluded. Limits for other values of ${\lambda}^{'}_{3ij}$ are obtained multiplying the ones shown by the ratio ${\lambda}^{'}_{3ij}$/0.5

Observed (solid line) and expected (dashed line) upper limits at 95% CL on the ${\lambda}_{132}$ coupling in the RPV SUSY model with a stau mediator, as a function of the mass of the stau, for the muon channel and for the production coupling, ${\lambda}^{'}_{3ij}$ = 0.5 . The couplings ${\lambda}^{'}_{3ij}$, ${\lambda}_{231}$, and ${\lambda}_{132}$ are defined in Fig. 1. The one (green) and two (yellow) standard deviation uncertainty bands for the expected limits are shown. The area above the limit curve is excluded. Limits for other values of ${\lambda}^{'}_{3ij}$ are obtained multiplying these ones by the ratio ${\lambda}^{'}_{3ij}$/0.5

Region in the oblique (W,Y) parameter phase space allowed by the current analysis at 95% CL. The combination of the electron and muon channel distributions from the 2017 and 2018 data sets at sqrt(s) = 13 TeV is used, having 101 fb-1 of luminosity.

Negative Log-Likelihood for the determination of the oblique W parameter, for the combination of the electron and muon channel and for 2017 and 2018 data sets (L=101 fb-1) at sqrt(s) = 13 TeV.

Total background estimate and observed data in all bins used in the limit calculation for the SSM WPRIME limits. The total event sample is split into the three years of data taking and the two lepton flavors.The distributions being shown are $M_T$ ones. The associated integrated luminosities for each year are of 35.9, 41.5, and 59.4 fb$̣^{-1}. The mass bins are numbered in ascending order.

Bayesian upper exclusion limits on the ratio $g_{W'}/g_{W}$ for an SSM-like W$\prime$ boson are shown. The unity coupling ratio (blue dotted curve) corresponds to the SSM common benchmark. The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian lower exclusion limits on the NUGIM G(221) mixing angle $\cot(\theta_{E})$ are shown as a function of the W$\prime$ boson mass. The theoretically excluded region is shaded in grey. The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian upper exclusion limits at 95% CL on the product of the production cross section and branching fraction of a QBH in an associated $ au$ lepton and neutrino final state. Minimum threshold masses $m_{th}$ of up to 6.6 TeV are excluded at 95% CL. The observed limit (solid line) is obtained from the intersection with the LO QBH cross section (blue dotted curve). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian upper limits at 95% CL on the cross section of the process $pp\rightarrow\tau\nu$ mediated via LQ exchange in the t-channel. The inner (green) band and the outer (yellow) band indicate the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The predicted LQ cross section at LO in the three coupling benchmark scenarios is depicted in different colors for $g_{U}=1$. The uncertainty bandy correspond to the sum in quadrature of PDF and scale variations. The first benchmark scenario considers only couplings to left-handed SM fermions (i.e. $\beta_{\text{R}}^{ij}=0$) and is referred to as "best fit LH". The second benchmark, referred to as "best fit LH+RH", considers $|\beta_{\text{R}}^{\text{b}\tau}|=1$ and all other $\beta_{\text{R}}^{ij}=0$. In the third "democratic" benchmark, equal couplings only to LH fermions are assumed, i.e. $\beta_{\text{L}}^{ij}=1$ and $\beta_{\text{R}}^{ij}=0$ for all $i$ and $j$.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the LH scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the LH+RH scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the democratic scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Bayesian upper exclusion limits at 95% CL on each of the Wilson coefficients described by the EFT model based on 2016-2018 data. The three different coupling types represent a left-handed vector coupling ($\epsilon^{cb}_{L}$), tensor-like coupling ($\epsilon^{cb}_{T}$), and scalar-tensor-like coupling ($\epsilon^{cb}_{S_{L}}$). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Summary of exclusion limits (expected and observed) calculated at 95% CL for full Run-2 CMS data.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "year_binnumber", following the binning from Figure 4.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "year_binnumber", following the binning used in Figure 4.

Predicted signal yields for the 2017 data-taking period, corresponding to 41.3 fb$^{-1}$, after the application of each search requirement (cumulative) for various signal hypothesis. The requirements listed are presented as total efficiencies w.r.t. the previous selection step.

Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).

Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).

Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).

Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).

Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker.

TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events.

TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker.

TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events.

The TD jet tagger score distributions for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The last bin contains jets with tagger scores greater than 0.996, the threshold used to tag signal jets. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The efficiency of the TD jet tagger working point used in the analysis is shown as a function of the lab frame transverse decay length for simulated signals with $\chi$ mass of 400 GeV. The uncertainties shown account for lifetime dependence and statistical uncertainty.

The TD jet tagger misidentification probability measured using the nominal $W$+jets MR is shown along with the systematic uncertainty, quantifying the degree of process dependence measured from alternative MRs. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.

The TD jet tagger score distributions for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. The last bin contains jets with tagger scores greater than 0.996, the threshold used to tag signal jets. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The TD jet tagger misidentification probability measured using the nominal $W$+jets MR is shown along with the systematic uncertainty, quantifying the degree of process dependence measured from alternative MRs. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.

The TD jet tagger misidentification probability measured using the nominal $W$+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well ($Z$+jets MR as green round markers, $t\bar{t}$ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.

Distribution of the number of TD tagged jets for the $m_{\chi} = 400$ GeVsimulated signal samples with $c\tau_{\chi} = 0.5$ m (solid red line) and $c\tau_{\chi} = 3.0$ m (dotted green line), estimated background (blue square markers), and data (black round markers). The blue shaded region indicates the systematic uncertainty in the background prediction. No background prediction is shown for the bin with zero TD tagged jets as it is the main control region used to predict the background for the other two bins. There are zero observed events in the bin with two or more TD tagged jets.

The TD jet tagger misidentification probability measured using the nominal $W$+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well ($Z$+jets MR as green round markers, $t\bar{t}$ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).

Distribution of the number of TD tagged jets for the $m_{\chi} = 400$ GeVsimulated signal samples with $c\tau_{\chi} = 0.5$ m (solid red line) and $c\tau_{\chi} = 3.0$ m (dotted green line), estimated background (blue square markers), and data (black round markers). The signal distributions are normalized to the expected cross section limit. The blue shaded region indicates the systematic uncertainty in the background prediction. No background prediction is shown for the bin with zero TD tagged jets as it is the main control region used to predict the background for the other two bins. There are zero observed events in the bin with two or more TD tagged jets.

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$. The area enclosed by the dotted black line corresponds to the observed excluded region.

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The distribution of the jet charged hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The distribution of the jet neutral hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The distribution of the number of track constituents in the jet, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The $\eta$ distribution of TD-tagged jets in a background-enriched control region that comprises events with exactly one TD-tagged jet. Observed data (black round markers) and the corresponding prediction based on control samples in data (empty squared markers), measured using the nominal $W$+jets MR, are compared. The prediction uncertainty (gray band) includes the systematic uncertainty quantifying the degree of process dependence measured from alternative MRs. The predictions for the shape and the normalization of the $\eta$ distribution are consistent with the data.

Jet time distribution in a sample of b-tagged jets from dilepton $t \bar{t}$ events in 2017 data-taking period (black round markers) and simulation (filled histogram). A Gaussian smearing procedure is applied to the jet time in the $t \bar{t}$ sample (green line) to correct for effects that are difficult to simulate (timing drift caused by crystals transparency loss due to detector aging, electronics jitter).

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 1$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.75$, $\mathcal{B}(\chi\to Z\tilde{G}) = 0.25$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.25$, $\mathcal{B}(\chi\to Z\tilde{G}) = 0.75$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to Z\tilde{G}) = 1$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 127 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 127 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 150 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 150 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 175 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 175 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 200 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 200 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 250 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 250 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 300 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 300 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 400 GeV.

Expected and observed upper limits on the AQGC operators $a^Z_C/\Lambda^2$, with no unitarization. The $y$ axis shows the limit on the ratio of the observed cross section to the cross section predicted for each anomalous coupling value ($\sigma_\mathrm{AQGC}$).

Limits on LEP-like dimension-6 anomalous quartic gauge coupling parameters, with and without unitarization via a clipping procedure.

Conversion of limits on $a^W_0$ to dimension-8 $f_{M,i}$ operators, using the assumption of vanishing $WWZ\gamma$ couplings to eliminate some parameters. When quoting limits on one of the operators, the other is fixed to zero. The results for $|f_{M,0}/\Lambda^{4}|$ and $|f_{M,4}/\Lambda^{4}|$ are shown with and without clipping of the signal model at 1.4 TeV, when the other parameter is fixed to the SM value of zero.

Conversion of limits on $a^W_0$ and $a^W_C$ to dimension-8 $f_{M,i}$ operators, using the assumption that all $f_{M,i}$ except one are equal to zero. The results are shown with and without clipping of the signal model at 1.4 TeV.

{Expected and observed limits in the two-dimensional plane of $a^W_0/\Lambda^2$ vs. $a^W_C/\Lambda^2$. The limits are described by analytical ellipses of equation $(x-x0)^2/a^2 + (y-y0)^2/b^2 = 1$ and rotated counter-clockwise by $\theta$ degrees, where $x$ and $y$ in the equation correspond to the $a_0^W$ and $a_C^W$ couplings, respectively.

{Expected and observed limits in the two-dimensional plane of $a^W_0/\Lambda^2$ vs. $a^W_C/\Lambda^2$ with unitarization imposed by clipping the signal model at 1.4 TeV. The limits are described by analytical ellipses of equation $(x-x0)^2/a^2 + (y-y0)^2/b^2 = 1$ and rotated counter-clockwise by $\theta$ degrees, where $x$ and $y$ in the equation correspond to the $a_0^W$ and $a_C^W$ couplings, respectively.

{Expected and observed limits in the two-dimensional plane of $a^Z_0/\Lambda^2$ vs. $a^Z_C/\Lambda^2$. The limits are described by analytical ellipses of equation $(x-x0)^2/a^2 + (y-y0)^2/b^2 = 1$ and rotated counter-clockwise by $\theta$ degrees, where $x$ and $y$ in the equation correspond to the $a_0^Z$ and $a_C^Z$ couplings, respectively.

Distribution of the MLP W+jets score in the CRs for the $T\overline{T}$ search. The observed data, predicted $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario, and the background are all shown. Statistical and systematic uncertainties in the background prediction before performing the fit to data are also shown.

Distribution of DeepAK8 jet tags in the DeepAK8 CR of the $T\overline{T}$ search. The observed data are shown using black markers, $T\overline{T}$ signals using solid lines, and background using filled histograms. Statistical and systematic uncertainties in the background prediction before performing the fit to data are shown by the hatched region.

Distribution of HT in the W+jets CR of the $T\overline{T}$ search. The observed data are shown using black markers, $T\overline{T}$ signals using solid lines, and background using filled histograms. Statistical and systematic uncertainties in the background prediction before performing the fit to data are shown by the hatched region.

Distribution of HT in the $T\overline{T}$ CR of the $T\overline{T}$ search. The observed data are shown using black markers, $T\overline{T}$ signals using solid lines, and background using filled histograms. Statistical and systematic uncertainties in the background prediction before performing the fit to data are shown by the hatched region.

Numbers of predicted and observed CR events in 2016--2018 data for the $T\overline{T}$ signal hypotheses in the single-lepton channel, after a background-only fit to data. Predicted numbers of signal events before the fit to data are included for comparison, using the singlet branching fraction scenario. Uncertainties include statistical and systematic components, and lepton flavor categories are combined with their uncertainties added in quadrature. Values in the DeepAK8 CR represent the number of large-radius jets rather than number of events.

Numbers of predicted and observed CR events in 2016--2018 data for the $B\overline{B}$ signal hypotheses in the single-lepton channel, after a background-only fit to data. Predicted numbers of signal events before the fit to data are included for comparison, using the singlet branching fraction scenario. Uncertainties include statistical and systematic components, and lepton flavor categories are combined with their uncertainties added in quadrature. Values in the DeepAK8 CR represent the number of large-radius jets rather than number of events.

Numbers of predicted and observed CR events in 2017--2018 data in the same-sign dilepton channel, after a background-only fit to data. Predicted numbers of signal events before the fit to data are included for comparison, using the singlet branching fraction scenario. Uncertainties include statistical and systematic components. Predictions for 2017 and 2018 are combined with their uncertainties added in quadrature.

Distribution of lepton $p_T$ in 2017 in the multilepton channel nonprompt lepton control region for all flavor categories, evaluated with the best-fit nonprompt rates. The uncertainty shown is the quadratic sum of statistical and systematics uncertainties.

Distribution of lepton $p_T$ in 2018 in the multilepton channel nonprompt lepton control region for all flavor categories, evaluated with the best-fit nonprompt rates. The uncertainty shown is the quadratic sum of statistical and systematics uncertainties.

Numbers of predicted and observed CR events in 2017--2018 data in the multilepton channel, after a background-only fit to data. Predicted numbers of signal events before the fit to data are included for comparison, using the singlet branching fraction scenario. Uncertainties include statistical and systematic components. Predictions for 2017 and 2018 are combined with their uncertainties added in quadrature.

Numbers of predicted and observed $T\overline{T}$ SR events in 2016--2018 data in the single-lepton channel, after a background-only fit to data. Predicted numbers of signal events before the fit to data are included for comparison, using the singlet branching fraction scenario. Uncertainties include statistical and systematic components, and lepton flavor categories are combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 1. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 2. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 3. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 4. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 5. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 6. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 7. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 8. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 9. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 10. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 11. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of the VLQ score in single-lepton category 12. Observed data is shown using black markers, $T\overline{T}$ signal with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region. Electron and muon categories have been combined with their uncertainties added in quadrature.

Distribution of $H_T^{\mathrm{lep}}$ in the same-sign dilepton signal region for $ee$ category. Data from 2017 and 2018 is shown using black markers, $T\overline{T}$ signals with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region.

Distribution of $H_T^{\mathrm{lep}}$ in the same-sign dilepton signal region for $e\mu$ category. Data from 2017 and 2018 is shown using black markers, $T\overline{T}$ signals with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region.

Distribution of $H_T^{\mathrm{lep}}$ in the same-sign dilepton signal region for $\mu\mu$ category. Data from 2017 and 2018 is shown using black markers, $T\overline{T}$ signals with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region.

Distribution of $S_T$ in the multilepton signal region for $eee$ category. Data from 2017 and 2018 is shown using black markers, $T\overline{T}$ signals with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region.

Distribution of $S_T$ in the multilepton signal region for $ee\mu$ category. Data from 2017 and 2018 is shown using black markers, $T\overline{T}$ signals with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region.

Distribution of $S_T$ in the multilepton signal region for $e\mu\mu$ category. Data from 2017 and 2018 is shown using black markers, $T\overline{T}$ signals with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region.

Distribution of $S_T$ in the multilepton signal region for $\mu\mu\mu$ category. Data from 2017 and 2018 is shown using black markers, $T\overline{T}$ signals with mass of 1.2 (1.5) TeV in the singlet scenario using solid (dashed) lines, and post-fit background using filled histograms. Statistical and systematic uncertainties in the background prediction after performing the fit to data are shown by the hatched region.

Numbers of predicted and observed $B\overline{B}$ SR events in 2016--2018 data in the single-lepton channel, after a background-only fit to data. Predicted numbers of signal events before the fit to data are included for comparison, using the singlet branching fraction scenario. Uncertainties include statistical and systematic components, and lepton flavor categories are combined with their uncertainties added in quadrature.

Numbers of predicted and observed SR events in 2017--2018 data in the same-sign dilepton channel, after a background-only fit to data. Predicted numbers of signal events before the fit to data are included for comparison, using the singlet branching fraction scenario. Uncertainties include statistical and systematic components. Predictions for 2017 and 2018 are combined with their uncertainties added in quadrature.

Numbers of predicted and observed SR events in 2017--2018 data in the multilepton channel, after a background-only fit to data. Predicted numbers of signal events before the fit to data are included for comparison, using the singlet branching fraction scenario. Uncertainties include statistical and systematic components. Predictions for 2017 and 2018 are combined with their uncertainties added in quadrature.

Expected and observed lower limits on the VLT quark masses.

Expected and observed lower limits on the VLB quark masses.

Expected 95% CL upper limits on $T\overline{T}$ production cross sections for the singlet combination.

Expected 95% CL upper limits on $T\overline{T}$ production cross sections for the doublet combination.

Expected 95% CL upper limits on $B\overline{B}$ production cross sections for the singlet combination.

Expected 95% CL upper limits on $B\overline{B}$ production cross sections for the doublet combination.

The 95% CL expected lower mass limits on pair-produced T quark masses as functions of their branching ratios to W and H bosons.

The 95% CL observed lower mass limits on pair-produced T quark masses as functions of their branching ratios to W and H bosons.

The 95% CL expected lower mass limits on pair-produced B quark masses as functions of their branching ratios to W and H bosons.

The 95% CL observed lower mass limits on pair-produced B quark masses as functions of their branching ratios to W and H bosons.

SM background observation/prediction in the WH signal region

SM background prediction vs. observation in the W signal region

SM background observation/prediction in the W signal region

SM background prediction vs. observation in the H signal region

SM background observation/prediction in the H signal region

Observed exclusion limits assuming the approximate-NLO+NLL cross sections

Expected exclusion limits assuming the approximate-NLO+NLL cross sections

The 95% CL observed upper limits on the production cross sections for $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^\mp_1$ assuming that each $\widetilde{\chi}^\pm_1$ decays to a W boson and $\widetilde{\chi}^0_1$

Observed exclusion limits assuming the approximate-NLO+NLL cross sections

Expected exclusion limits assuming the approximate-NLO+NLL cross sections

The 95% CL observed upper limits on the production cross sections for $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^0_2$ assuming that each $\widetilde{\chi}^\pm_1$ decays to a W boson and $\widetilde{\chi}^0_1$ and the $\widetilde{\chi}^0_2$ decays to a Z boson and $\widetilde{\chi}^0_1$

Observed exclusion limits assuming the approximate-NLO+NLL cross sections

Expected exclusion limits assuming the approximate-NLO+NLL cross sections

The 95% CL observed upper limits on the production cross sections for $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^0_2$ assuming that each $\widetilde{\chi}^\pm_1$ decays to a W boson and $\widetilde{\chi}^0_1$ and the $\widetilde{\chi}^0_2$ decays to a H boson and $\widetilde{\chi}^0_1$

Observed exclusion limits assuming the approximate-NLO+NLL cross sections

Expected exclusion limits assuming the approximate-NLO+NLL cross sections

Observed exclusion limits assuming the approximate-NLO+NLL cross sections

Expected exclusion limits assuming the approximate-NLO+NLL cross sections

Observed exclusion limits assuming the approximate-NLO+NLL cross sections

Expected exclusion limits assuming the approximate-NLO+NLL cross sections

The 95% CL observed upper limits on the production cross sections for mass-degenerate higgsino-like$\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^\mp_1$, $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^0_2$, $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^0_3$ and $\widetilde{\chi}^0_2$ $\widetilde{\chi}^0_3$ as functions of the NLSP and LSP masses.

Efficiency of bb-tagger for H(bb), Z(bb) and Z(cc) decays.

Efficiency of W- and V-tagger for W(qq) and Z(qq) decays.

Acceptance times efficiency values with statistical uncertainties for TChiWW in the b-Veto region.

Acceptance times efficiency values with statistical uncertainties for TChiWZ in the b-Veto region.

Acceptance times efficiency values with statistical uncertainties for TChiWH in the b-Veto region.

Acceptance times efficiency values with statistical uncertainties for TChiHZ in the b-Veto region.

Acceptance times efficiency values with statistical uncertainties for TChiWW in the WHSR region.

Acceptance times efficiency values with statistical uncertainties for TChiWW in the WSR region.

Acceptance times efficiency values with statistical uncertainties for TChiWW in the HSR region.

Acceptance times efficiency values with statistical uncertainties for TChiWZ in the WHSR region.

Acceptance times efficiency values with statistical uncertainties for TChiWZ in the WSR region.

Acceptance times efficiency values with statistical uncertainties for TChiWZ in the HSR region.

Acceptance times efficiency values with statistical uncertainties for TChiWH in the WHSR region.

Acceptance times efficiency values with statistical uncertainties for TChiWH in the WSR region.

Acceptance times efficiency values with statistical uncertainties for TChiWH in the HSR region.

Acceptance times efficiency values with uncertainties for TChiHZ in the WHSR region.

Acceptance times efficiency values with uncertainties for TChiHZ in the WSR region.

Acceptance times efficiency values with statistical uncertainties for TChiHZ in the HSR region.

Covariance matrix for the signal regions, derived from a fit to the control regions only under the background-only hypothesis.

Correlation matrix for the signal regions, derived from a fit to the control regions only under the background-only hypothesis.

The observed and expected (in parentheses) 95% CL lower mass limits on RPV SUSY, Z′ ($\mathcal{B}=0.1$) , and QBH signals for the e$\mu$, e$\tau$, and $\mu\tau$ channels.

Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.

Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.

Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the e$\mu$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the e$\tau$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the $\mu\tau$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.

Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.

Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the e$\mu$ channel. The regions to the left of and above the curves are excluded.

Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the e$\tau$ channel. The regions to the left of and above the curves are excluded.

Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the $\mu\tau$ channel. The regions to the left of and above the curves are excluded.

Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.

Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.

Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.

Upper limits at 95% CL on $\sigma\times\mathcal{B}^2(\mathrm{H}_1\rightarrow b\bar{b})$ as a function of $m_{\mathrm{H_1}}$ for $m_{\mathrm{SUSY}}=1600\;\mathrm{GeV}$.

Upper limits at 95% CL on $\sigma\times\mathcal{B}^2(\mathrm{H}_1\rightarrow b\bar{b})$ as a function of $m_{\mathrm{H_1}}$ for $m_{\mathrm{SUSY}}=2000\;\mathrm{GeV}$.

Upper limits at 95% CL on $\sigma\times\mathcal{B}^2(\mathrm{H}_1\rightarrow b\bar{b})$ as a function of $m_{\mathrm{H_1}}$ for $m_{\mathrm{SUSY}}=2200\;\mathrm{GeV}$.

Upper limits at 95% CL on $\sigma\times\mathcal{B}^2(\mathrm{H}_1\rightarrow b\bar{b})$ as a function of $m_{\mathrm{H_1}}$ for $m_{\mathrm{SUSY}}=2400\;\mathrm{GeV}$.

Upper limits at 95% CL on $\sigma\times\mathcal{B}^2(\mathrm{H}_1\rightarrow b\bar{b})$ as a function of $m_{\mathrm{H_1}}$ for $m_{\mathrm{SUSY}}=2600\;\mathrm{GeV}$.

Upper limits at 95% CL on $\sigma\times\mathcal{B}^2(\mathrm{H}_1\rightarrow b\bar{b})$ as a function of $m_{\mathrm{H_1}}$ for $m_{\mathrm{SUSY}}=2800\;\mathrm{GeV}$.

Expected event yields in each $m_{jj}$ bin for the different background processes in the SR of the VTR category, in the 2017 and 2018 samples. The background yields and the corresponding uncertainties are obtained after performing a combined fit across all of the CRs and SR. The expected signal contributions for a Higgs boson, produced in the non-VBF and VBF modes, decaying to invisible particles with a branching fraction of $\mathcal{B}(\text{H} \to \text{inv}) = 1$, and the observed event yields are also reported.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2017, signal region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2017, dimuon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2018, signal region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2018, dimuon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2017, signal region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2017, dimuon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2018, signal region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2018, single electron region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2018, dielectron region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2018, dimuon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2017, photon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2018, photon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2017, single electron region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2017, single muon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2017, dielectron region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2018, single electron region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2018, single muon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for MTR, 2018, dielectron region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2017, single electron region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2017, single muon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2017, dielectron region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Event yields for the postfit background model from the background-only and signal-plus-background fits, as well as the observed event yields for VTR, 2018, single muon region. All signal and control regions from the 2017 and 2018 data sets are included in the fit.

Trigger efficiency measured as a function of $p_{T}^{miss}$ in data for the 2017 and 2018 samples for the MTR category. The efficiency is measured in a phase space with at least two jets in which the leading (trailing) jet has $p_{T}>80~\text{GeV}$ ($>40~\text{GeV}$). The two jets are required to form a system with $m_{jj}>200~\text{GeV}$ and $\Delta\Phi_{jj}<1.5$.

Trigger efficiency measured as a function of $p_{T}^{miss}$ in data for the 2017 and 2018 samples for the MTR category. The efficiency is measured in a phase space with at least two jets in which the leading (trailing) jet has $p_{T}>140~\text{GeV}$ ($>70~\text{GeV}$). The two jets are required to form a system with $m_{jj}>900~\text{GeV}$ and $\Delta\Phi_{jj}<1.8$.

Measured inclusive fiducial $tt\gamma$ production cross section in the dilepton final state for the different dilepton-flavour channels and combined.

Absolute differential $tt\gamma$ production cross section as a function of $p_{T}(\gamma)$ . The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $|\eta |(\gamma)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of min $\Delta R(\gamma, \ell)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $\Delta R(\gamma, \ell_{1})$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $\Delta R(\gamma, \ell_{2})$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of min $\Delta R(\gamma, b)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $|\Delta\eta(\ell\ell)|$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $\Delta \phi(\ell\ell)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $p_{T}(\ell\ell) $. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $p_{T}(\ell_{1})+p_{T}(\ell_{2})$ . The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of min $\Delta R(\ell, j)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $p_{T}(j_{1})$ .

Normalized differential $tt\gamma$ production cross section as a function of $p_{T}(\gamma)$ . The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $|\eta |(\gamma)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of min $\Delta R(\gamma, \ell)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $\Delta R(\gamma, \ell_{1})$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $\Delta R(\gamma, \ell_{2})$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of min $\Delta R(\gamma, b)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $|\Delta\eta(\ell\ell)|$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $\Delta \phi(\ell\ell)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $p_{T}(\ell\ell) $. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $p_{T}(\ell_{1})+p_{T}(\ell_{2})$ . The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of min $\Delta R(\ell, j)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $p_{T}(j_{1})$ . The values provided in the table are not divided by the bin width.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $p_{T}(\gamma)$ .

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $p_{T}(\gamma)$ .

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $|\eta |(\gamma)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $|\eta |(\gamma)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of min $\Delta R(\gamma, \ell)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of min $\Delta R(\gamma, \ell)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $\Delta R(\gamma, \ell_{1})$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $\Delta R(\gamma, \ell_{1})$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $\Delta R(\gamma, \ell_{2})$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $\Delta R(\gamma, \ell_{2})$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of min $\Delta R(\gamma, b)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of min $\Delta R(\gamma, b)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $|\Delta\eta(\ell\ell)|$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $|\Delta\eta(\ell\ell)|$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $\Delta \phi(\ell\ell)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $\Delta \phi(\ell\ell)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $p_{T}(\ell\ell) $.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $p_{T}(\ell\ell) $.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $p_{T}(\ell_{1})+p_{T}(\ell_{2})$ .

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $p_{T}(\ell_{1})+p_{T}(\ell_{2})$ .

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of min $\Delta R(\ell, j)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of min $\Delta R(\ell, j)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $p_{T}(j_{1})$ .

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $p_{T}(j_{1})$ .

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c_{tZ}$, using the photon pT distribution from the dilepton analysis. The value of $c^{I}_{tZ}$ is fixed to zero in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c_{tZ}$, using the combination of photon pT distributions from the dilepton and lepton+jets analyses. The value of $c^{I}_{tZ}$ is fixed to zero in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c^{I}_{tZ}$, using the photon pT distribution from the dilepton analysis. The value of $c_{tZ}$ is fixed to zero in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c^{I}_{tZ}$, using the combination of photon pT distributions from the dilepton and lepton+jets analyses. The value of $c_{tZ}$ is fixed to zero in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c_{tZ}$, using the photon pT distribution from the dilepton analysis. The value of $c^{I}_{tZ}$ is profiled in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c_{tZ}$, using the combination of photon pT distributions from the dilepton and lepton+jets analyses. The value of $c^{I}_{tZ}$ is profiled in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c^{I}_{tZ}$, using the photon pT distribution from the dilepton analysis. The value of $c_{tZ}$ is profiled in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c^{I}_{tZ}$, using the combination of photon pT distributions from the dilepton and lepton+jets analyses. The value of $c_{tZ}$ is profiled in the fit.

Negative log-likelihood difference from the best-fit value as a function of Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$ from the interpretation of the dilepton measurement.

Negative log-likelihood difference from the best-fit value as a function of Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$ from the interpretation of the dilepton and lepton+jets measurements combined.

One-dimensional 68 and 95% CL intervals obtained for the Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$, using the photon $p_{T}$ distribution from the dilepton analysis, or the combination of photon pT distributions from the dilepton and lepton+jets analyses.

Comparison of observed $95\%$ CL intervals for the Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$. Results are shown from a CMS ttZ measurement [JHEP 03 (2020) 056], from a CMS ttZ & tZq interpretation [arXiv:2107.13896], from a CMS ttG (lepton+jets) measurement [arXiv:2107.01508], from this measurement, and from a global fit by J. Ellis et al. [JHEP 04 (2021) 279].

Number of predicted signal events in the $\mu\mu X$ final states, for several benchmark signal hypotheses for Majorana HNL. The quoted uncertainties include statistical and systematic uncertainties.

The 95% CL limits on $|V_{Ne}|^2$ as a function of the HNL mass for a Majorana HNL. Values of $-1$ indicate that no limit is available for the mass point.

The 95% CL limits on $|V_{N\mu}|^2$ as a function of the HNL mass for a Majorana HNL. Values of $-1$ indicate that no limit is available for the mass point.

The 95% CL limits on $|V_{Ne}|^2$ as a function of the HNL mass for a Dirac HNL. Values of $-1$ indicate that no limit is available for the mass point.

The 95% CL limits on $|V_{N\mu}|^2$ as a function of the HNL mass for a Dirac HNL. Values of $-1$ indicate that no limit is available for the mass point.

Covariance matrix for the background prediction in the signal region bins for the $eeX$ final states. The bin labels specify the channel, the $m(\ell_2,\ell_3)$ bin, and the $\Delta_{\mathrm{2D}}$ bin.

Covariance matrix for the background prediction in the signal region bins for the $\mu\mu X$ final states. The bin labels specify the channel, the $m(\ell_2,\ell_3)$ bin, and the $\Delta_{\mathrm{2D}}$ bin.

Correlation matrix for the background prediction the signal region bins for the $eeX$ final states. The bin labels specify the channel, the $m(\ell_2,\ell_3)$ bin, and the $\Delta_{\mathrm{2D}}$ bin.

Correlation matrix for the background prediction the signal region bins for the $\mu\mu X$ final states. The bin labels specify the channel, the $m(\ell_2,\ell_3)$ bin, and the $\Delta_{\mathrm{2D}}$ bin.

Mean lifetime and production cross section for HNLs of Majorana nature, assuming the decay to three charged leptons and coupling to only electrons or muons, as evaluated from simulated event samples.

Theory cross sections vs m($\widetilde{\chi}^0_1$) for SMS model TChiHH-G, $\widetilde{\chi}^0_1, \widetilde{\chi}^0_2$ NLSPs only.

The 95% CL observed and expected upper limits on the production cross sections of the TChiHH signal model as a function of the $\widetilde{\chi}^0_2$ and $\widetilde{\chi}^0_1$ masses. Relative to Fig. 13, the binning of the table is adjusted to align with the model points that were sampled. In addition, a small correction has been applied that affects the cross section upper limits for points with $m(\widetilde{\chi}^0_1) = 1$ GeV. For that row of points in the figure, the small region of model phase space reached by both the resolved and boosted selections was inadvertently double counted in computing the signal contribution to the predicted yield.

Exclusion limits assuming the approximate-NNLO+NNLL cross sections

Exclusion limits assuming the approximate-NNLO+NNLL cross sections

Exclusion limits assuming the approximate-NNLO+NNLL cross sections

Exclusion limits assuming the approximate-NNLO+NNLL cross sections

Exclusion limits assuming the approximate-NNLO+NNLL cross sections

Exclusion limits assuming the approximate-NNLO+NNLL cross sections

Cross section, 95% CL upper limit plus theory vs m($\widetilde{g}$) for SMS model T5HH.

Pre-fit background covariance matrix $\sigma_{xy}$ for the 22 analysis bins, ordered as in Fig. 10.

Pre-fit background correlation matrix $\rho_{xy}$ for the 22 analysis bins, ordered as in Fig. 10.

Significance vs $(m(\widetilde{\chi}^0_2), m(\widetilde{\chi}^0_1))$ for SMS model TChiHH.

Significance vs $m(\widetilde{g})$ for SMS model T5HH.

Likelihood profile at $(m(\widetilde{\chi}^0_2) = $275, $m(\widetilde{\chi}^0_1) = $1) for SMS model TChiHH.

Likelihood profile at $(m(\widetilde{\chi}^0_2) = $300, $m(\widetilde{\chi}^0_1) = $1) for SMS model TChiHH.

Likelihood profile at $(m(\widetilde{\chi}^0_2) = $300, $m(\widetilde{\chi}^0_1) = $50) for SMS model TChiHH.

Likelihood profile at $(m(\widetilde{\chi}^0_2) = $450, $m(\widetilde{\chi}^0_1) = $1) for SMS model TChiHH.

Likelihood profile at $(m(\widetilde{\chi}^0_2) = $750, $m(\widetilde{\chi}^0_1) = $1) for SMS model TChiHH.

Likelihood profile at $(m(\widetilde{\chi}^0_2) = $750, $m(\widetilde{\chi}^0_1) = $200) for SMS model TChiHH.

Efficiency vs $m(\widetilde{\chi}^0_1)$ for SMS model TChiHH-G. The denominator includes all 22 signal regions, and assumes $\mathcal{B}(\mathrm{H}$-->$\mathrm{b}\overline{\mathrm{b}})=100\%$.

Efficiency vs $m(\widetilde{g})$ for SMS model T5HH. The denominator includes all 22 signal regions, and no constraint on $\mathcal{B}(\mathrm{H}$-->$\mathrm{b}\overline{\mathrm{b}})$.

Efficiency vs $(m(\widetilde{\chi}^0_2), m(\widetilde{\chi}^0_1))$ for SMS model TChiHH. The denominator includes all 22 signal regions, and assumes $\mathcal{B}(\mathrm{H}$-->$\mathrm{b}\overline{\mathrm{b}})=100\%$.

Signal and control region populations for the resolved signature.

Signal and control region populations for the boosted signature.

Cut flow table for the resolved topology showing expected event yields at 137 fb$^{-1}$ for selected signal models as successive selections are applied.

Cut flow table for the boosted topology showing expected event yields at 137 fb$^{-1}$ for selected signal model points as successive selections are applied. Here the hadronic baseline is defined by $N_{\mathrm{jet}}\geq$2, \ptmiss$>$300 GeV, $\Delta\phi$ cuts, and the isolated lepton and track vetoes.

Expected and observed limits at 95% CL on the branching fraction B(hb --> PHI X) . B(PHI --> mu+mu-) as a function of signal mass for signal lifetime of 100 mm

Expected and observed limits at 95% CL on the branching fraction B(H --> ZD ZD) . B(ZD --> mu+mu-) as a function of signal mass for signal lifetime of 1 mm

Expected and observed limits at 95% CL on the branching fraction B(H --> ZD ZD) . B(ZD --> mu+mu-) as a function of signal mass for signal lifetime of 1 mm

Expected and observed limits at 95% CL on the branching fraction B(H --> ZD ZD) . B(ZD --> mu+mu-) as a function of signal mass for signal lifetime of 100 mm

Expected and observed limits at 95% CL on the branching fraction B(H --> ZD ZD) . B(ZD --> mu+mu-) as a function of signal mass for signal lifetime of 100 mm

Expected and observed limits at 95% CL on the branching fraction B(H --> ZD ZD) as a function of signal mass for signal lifetime of 1 mm

Expected and observed limits at 95% CL on the branching fraction B(H --> ZD ZD) as a function of signal mass for signal lifetime of 1 mm

Expected and observed limits at 95% CL on the branching fraction B(H --> ZD ZD) as a function of signal mass for signal lifetime of 100 mm

Expected and observed limits at 95% CL on the branching fraction B(H --> ZD ZD) as a function of signal mass for signal lifetime of 100 mm

Observed limits at 95% CL on the branching fraction B(hb --> PHI X) . B(PHI --> mu+mu-) in the parameter space containing signal mass and signal lifetime. The ROOT file containing the values are available as additional resource

Observed limits at 95% CL on the branching fraction B(hb --> PHI X) . B(PHI --> mu+mu-) in the parameter space containing signal mass and signal lifetime. The ROOT file containing the values is available as additional resource

Observed limits at 95% CL on the branching fraction B(H --> ZD ZD) in the parameter space containing signal mass and signal lifetime. The ROOT file containing the values are available as additional resource

Observed limits at 95% CL on the branching fraction B(H --> ZD ZD) in the parameter space containing signal mass and signal lifetime. The ROOT file containing the values is available as additional resource

Region excluded at 95% CL using this search for the inflaton model in the parameter space containing signal mass and square of the signal mixing angle

Region excluded at 95% CL using this search for the inflaton model in the parameter space containing signal mass and square of the signal mixing angle

Observed limits at 95% CL on the branching fraction B(H --> ZD ZD) in the parameter space containing signal mass and kinetic mixing parameter. The ROOT file containing the values are available as additional resource

Observed limits at 95% CL on the branching fraction B(H --> ZD ZD) in the parameter space containing signal mass and kinetic mixing parameter. The ROOT file containing the values is available as additional resource

Observed limits at 95% CL on the branching fraction B(hb --> PHI X) . B(PHI --> mu+mu-) as a function of signal lifetime for various representative signal mass hypotheses

Observed limits at 95% CL on the branching fraction B(H --> ZD ZD) as a function of signal lifetime for various representative signal mass hypotheses

CMS and LHCb observed limits at 95% CL on the branching fraction B(hb --> PHI X) . B(PHI --> mu+mu-) as a function of signal mass for signal lifetime of 1 mm

CMS and LHCb observed limits at 95% CL on the branching fraction B(hb --> PHI X) . B(PHI --> mu+mu-) as a function of signal mass for signal lifetime of 10 mm

CMS and LHCb observed limits at 95% CL on the branching fraction B(hb --> PHI X) . B(PHI --> mu+mu-) as a function of signal mass for signal lifetime of 100 mm

Observed limits at 95% CL on number of signal events in aggregate signal region 0.2 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 0.2 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 0.2 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 0.2 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 1.0 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 1.0 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 1.0 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 1.0 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 2.4 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 2.4 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 2.4 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 2.4 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 3.1 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 3.1 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 3.1 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 3.1 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 7.0 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 7.0 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 0 GeV and two isolated muons

Observed limits at 95% CL on number of signal events in aggregate signal region 7.0 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and no requirement on muon isolation

Observed limits at 95% CL on number of signal events in aggregate signal region 7.0 cm $\leq$ lxy < 11.0 cm, dimuon pt $\geq$ 25 GeV and two isolated muons

The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the characteristic variable $\Delta\phi_{\text{min}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $m_{\text{SD}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $D_{p_{\text{T}}}$ for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.

The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.

The normalized BDT discriminator distribution for the two highest $p_{\text{T}}$ jets from the simulated SM backgrounds and several signal models.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The BDT ROC curves for the two highest $p_{\text{T}}$ jets, comparing the simulated SM backgrounds with one signal model with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$.

The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the high-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{3}(x) = \exp(p_{1}x)x^{p_{2}(1+p_{3}\ln(x))}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the low-$R_{\text{T}}$ signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the high-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The $m_{\text{T}}$ distribution for the low-SVJ2 signal region, comparing the observed data to the background prediction from the analytic fit ($g_{2}(x) = \exp(p_{1}x)x^{p_{2}}$, $x = m_{\text{T}}/\sqrt{s}$). The distributions from several example signal models, with cross sections corresponding to the observed limits, are superimposed.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the dark hadron mass.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the inclusive search for variations of the mediator mass and the invisible fraction.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the dark hadron mass.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.

The 95% CL observed upper limits on the product of the cross section and branching fraction from the BDT-based search for variations of the mediator mass and the invisible fraction.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The observed exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 68% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The lower 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The upper 95% expected exclusion for the nominal $\text{Z}^{\prime}$ cross section.

The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.

The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.

The 95% CL upper limits on the product of the cross section and branching fraction from the inclusive search for the $\alpha_{\text{dark}}$ variations.

The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.

The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.

The 95% CL upper limits on the product of the cross section and branching fraction from the BDT-based search for the $\alpha_{\text{dark}}$ variations.

The three two-dimensional signal model parameter scans.

The three two-dimensional signal model parameter scans.

The three two-dimensional signal model parameter scans.

Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.

Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.

Metrics representing the performance of the BDT for the benchmark signal model ($m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$), compared to each of the major SM background processes.

The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.

The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.

The range of effects on the signal yield for each systematic uncertainty and the total. Values less than 0.01% are rounded to 0.0%.

The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $m_{\text{T}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $\Delta\eta(\text{J}_{1},\text{J}_{2})$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $p_{\text{T}}^{\text{miss}}$ for the simulated SM backgrounds and several signal models. The $R_{\text{T}}$ requirement is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $N_{\text{e}}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of the variable $N_{\mu}$ for the simulated SM backgrounds and several signal models. The requirement on this variable is omitted, but all other preselection requirements are applied. The last bin of each histogram includes the overflow events.

The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.

The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.

The normalized distribution of $\Delta\eta(\text{J}_{1},\text{J}_{2})$ vs. $R_{\text{T}}$ for the simulated QCD background. The preselection requirements on both variables are omitted, but all other preselection requirements are applied.

The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.

The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.

The normalized distribution of $p_{\text{T}}^{\text{miss}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.

The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.

The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.

The normalized distribution of $R_{\text{T}}$ vs. $m_{\text{T}}$ for the simulated QCD background. All selection requirements are omitted, except for the requirement of two high-$p_{\text{T}}$ wide jets.

The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\tau_{21}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\tau_{32}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $N_{2}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $N_{3}^{(1)}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{dark}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $g_{\text{jet}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\sigma_{\text{major}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\sigma_{\text{minor}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $\Delta\phi(\vec{J},\vec{p}_{\text{T}}^{\text{miss}})$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $r_{\text{inv}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{h}^{\pm}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{e}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\mu}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\text{h}^{0}}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The normalized distributions of the BDT input variable $f_{\gamma}$ for the two highest $p_{\text{T}}$ wide jets from the simulated SM backgrounds and several signal models with varying $m_{\text{Z}^{\prime}}$ values. Each sample's jet $p_{\text{T}}$ distribution is weighted to match a reference distribution (see text). The last bin of each histogram includes the overflow events.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.

The product of signal acceptance and efficiency in the high-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.

The product of signal acceptance and efficiency in the low-$R_{\text{T}}$ signal region, for variations of the mediator mass and the dark coupling strength.

The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the high-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.