Observed and expected 95% CL upper limits on the product of the production cross section ($\sigma$) and the branching fraction, obtained after combining all categories with 138 $\mathrm{fb}^{−1}$ of data at $\sqrt{s}$ = 13 TeV for $\mathrm{V'}$ to $VV$ + $VH$ signal in HVT model C.

Total signal efficiency including all event categories as a function of the resonance mass $M_X$ for all DY/ggF signal models under consideration. The estimates from MC simulation (markers) are interpolated with a functional form (lines).

Total signal efficiency including all event categories as a function of the resonance mass $M_X$ for all VBF signal models under consideration. The estimates from MC simulation (markers) are interpolated with a functional form (lines).

Measured ratio of $\mathcal{B}_{4\mu}/\mathcal{B}_{2\mu}$

Measured branching fraction $\mathcal{B}_{4\mu}$

Measured branching fraction $\mathcal{B}_{4\mu}$

Distributions of the b-tagging efficiency as a function of the mistag rate of charm jets for pp collisions in a PYTHIA simulation.

Distributions of the b-tagging efficiency as a function of the mistag rate of light jets for pPb collisions in a PYTHIA+HIJING simulation.

Distributions of the b-tagging efficiency as a function of the mistag rate of charm jets for pPb collisions in a PYTHIA+HIJING simulation.

Distributions of the Secondary Vertex Mass after applying the SSV tagger selection.

b-jet cross-sections in pPb, scaled by the Glauber nuclear overlap function, as well as a b-jet cross-section from a PYTHIA simulation from -2.5 < eta_CM < -1.5.

b-jet cross-sections in pPb, scaled by the Glauber nuclear overlap function, as well as a b-jet cross-section from a PYTHIA simulation from -1.5 < eta_CM < -0.5 (x 10^2).

b-jet cross-sections in pPb, scaled by the Glauber nuclear overlap function, as well as a b-jet cross-section from a PYTHIA simulation from -0.5 < eta_CM < 0.5 (x10^4).

b-jet cross-sections in pPb, scaled by the Glauber nuclear overlap function, as well as a b-jet cross-section from a PYTHIA simulation from 0.5 < eta_CM < 1.5 (x10^6).

b-jet RpA (PYTHIA) from 0.5 < eta_CM < 1.5.

b-jet RpA (PYTHIA) from -0.5 < eta_CM < 0.5.

b-jet RpA (PYTHIA) from -1.5 < eta_CM < -0.5.

b-jet RpA (PYTHIA) from -2.5 < eta_CM < -1.5.

b-jet Fraction.

b-jet RpA(PYTHIA).

Differential cross section dsigma(Z+c)/dpTZ times branching ratio and differential cross sections ratio dsigma(Z+c)/dpTZ / dsigma(Z+b)/dpTZ for three ranges of the transverse momentum of the Z boson and combining the dielectron and dimuon Z0 decay channels

Differential cross section dsigma(Z+c)/dpTjet times branching ratio and differential cross sections ratio dsigma(Z+c)/dpTZ / dsigma(Z+b)/dpTjet for three ranges of the transverse momentum of the jet and combining the dielectron and dimuon Z0 decay channels

Transverse momentum distribution of the selected muon-inside-a-jet for events with an identified muon among the jet constituents in the dielectron channel

Transverse momentum distribution of the selected muon-inside-a-jet for events with an identified muon among the jet constituents in the dimuon channel

The invariant mass distribution of three-prong secondary vertices for events selected in the D± mode, in the dielectron channel

The invariant mass distribution of three-prong secondary vertices for events selected in the D ± mode, in the dimuon channel

The invariant mass distribution of the three-track system composed of a two-prong secondary vertex and a primary particle for events selected in the D∗(2010)± mode, in the dielectron channel

The invariant mass distribution of the three-track system composed of a two-prong secondary vertex and a primary particle for events selected in the D ∗ (2010) ± mode, in the dimuon channel

Transverse momentum distribution of the c-tagged jet normalized to unity, in simulated W+c and Z+c samples and in W+c data events.

Number of reconstructed secondary vertices normalized to unity, in simulated W+c and Z+c samples and in W+c data events.

Distribution of the corrected secondary-vertex mass normalized to unity, in simulated W + c and Z + c samples, and in W + c data events.

Distribution of the JP discriminant for the D± mode normalized to unity, in simulated W + c and Z + c samples, and in W + c data events.

Distribution of the JP discriminant for the D∗(2010) ± mode normalized to unity, in simulated W + c and Z + c samples, and in W + c data events.

Distribution of the corrected secondary-vertex mass normalized to unity from simulated Z + b and eμ-tt data events

Corrected secondary-vertex mass distributions, after background subtraction in the dielectron channel for events selected in the semileptonic mode.

Corrected secondary-vertex mass distributions, after background subtraction in the dimuon channel for events selected in the semileptonic mode.

Background-subtracted distributions of the JP discriminant in the dielectron channel for Z + jets events with a D± → K∓ π± π± candidate.

Background-subtracted distributions of the JP discriminant in the dimuon channel for Z + jets events with a D± → K∓ π± π± candidate.

Background-subtracted distributions of the JP discriminant in the dielectron channel for Z +jets events with a D∗(2010)± → D0 π± → K∓ π± π± candidate.

Background-subtracted distributions of the JP discriminant in the dimuon channel for Z +jets events with a D∗(2010)± → D0 π± → K∓ π± π± candidate.

ontributions to the systematic uncertainty in the measured Z+c cross section and in the (Z+c)/(Z+b) cross section ratio.

Differential Z + c cross section as a function of the transverse momentum of the Z boson

(Z +c)/(Z +b) cross section ratio as a function of the transverse momentum of teh Z boson

Differential Z + c cross section as a function of the transverse momentum of the jet

(Z +c)/(Z +b) cross section ratio as a function of the transverse momentum of the jet.

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 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 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 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 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 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 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 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 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 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 55 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The cluster efficiency in bins of hadronic and EM energy in region A. Region A is defined as 391 cm $< r <$ 695.5 cm and 400 cm $< |z| <$ 671 cm. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The cluster efficiency in bins of hadronic and EM energy in region A. Region A is defined as 391 cm $< r <$ 695.5 cm and 400 cm $< |z| <$ 671 cm. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The cluster efficiency in bins of hadronic and EM energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The cluster efficiency in bins of hadronic and EM energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The efficiency of $N_{station} > 1$ requirement in bins of hadronic energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bin corresponds to LLPs that decayed leptonically with 0 hadronic energy. The efficiency is calculated with respect to clusters that pass all cluster-level cuts described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 10%.

The efficiency of $N_{station} > 1$ requirement in bins of hadronic energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bin corresponds to LLPs that decayed leptonically with 0 hadronic energy. The efficiency is calculated with respect to clusters that pass all cluster-level cuts described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 10%.

The geometric signal acceptance as a function of $c\tau$. The acceptance is shown for four different LLP mass hypotheses: 7, 15, 40, and 55 GeV. The acceptance is defined by requiring at least 1 LLP to decay in the region defined as 400 cm $< |z| <$ 1100 cm, $r < $ 695.5 cm, and $|\eta| <$ 2.4.

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.

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.

The product of signal acceptance and efficiency in the high-SVJ2 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 coupling strength.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark hadron mass.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the invisible fraction.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.

The product of signal acceptance and efficiency in the low-SVJ2 signal region, for variations of the mediator mass and the dark coupling strength.

Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.

Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.

Comparison of different the dijet mass $m_{\text{J}\text{J}}$, the transverse mass $m_{\text{T}}$, and the Monte Carlo (MC) mass $m_{\text{MC}}$ for a signal model with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. No selection is applied, except that there must be at least two jets. $m_{\text{MC}}$ is computed by adding the generator-level four-vectors for invisible particles to the dijet system, to represent the achievable resolution if the invisible component were fully measured. The last bin of each histogram includes the overflow events.

$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $m_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $r_{\text{inv}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the high-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

$m_{\text{T}}$ distributions for signal models with different $\alpha_{\text{dark}}$ values for the low-$R_{\text{T}}$ inclusive signal region.

The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.

The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.

The proportions of each SM background process in the high-$R_{\text{T}}$ signal region.

The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.

The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.

The proportions of each SM background process in the low-$R_{\text{T}}$ signal region.

The proportions of each SM background process in the high-SVJ2 signal region.

The proportions of each SM background process in the high-SVJ2 signal region.

The proportions of each SM background process in the high-SVJ2 signal region.

The proportions of each SM background process in the low-SVJ2 signal region.

The proportions of each SM background process in the low-SVJ2 signal region.

The proportions of each SM background process in the low-SVJ2 signal region.

The 95% CL expected 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 expected 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 expected 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 expected 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 expected 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 expected 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 expected 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 expected 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 expected 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 expected 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 expected 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 expected 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.

Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for the major background processes. Statistical uncertainties, at most 1.8%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.5%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, varying $m_{\text{dark}}$ values, $r_{\text{inv}} = 0.3$, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 2.6%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 1.2%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, varying $r_{\text{inv}}$ values, and $\alpha_{\text{dark}} = \alpha_{\text{dark}}^{\text{peak}}$. Statistical uncertainties, at most 0.9%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 2.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 3.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Relative efficiencies in % for each step of the event selection process for signals with $m_{\text{Z}^{\prime}} = 4.1\,\text{TeV}$, $m_{\text{dark}} = 20\,\text{GeV}$, $r_{\text{inv}} = 0.3$, and varying $\alpha_{\text{dark}}$ values. Statistical uncertainties, at most 0.4%, are omitted. The line "Efficiency [%]" is the absolute efficiency after the final selection. The subsequent lines show the efficiency for each signal region, relative to the final selection.

Observed and expected numbers of events with exactly three leptons with the scalar sum of jet transverse momentum values HT < 200 GeV. "On-Z" refers to events with at least one E+ E- or MU+ MU- (OSSF) pair with dilepton mass between 75 and 105 GeV, while "Off-Z" refers to events with one or two OSSF pairs, none of which fall in this mass range. The OSSFN designation refers to the number of E+ E- and MU+ MU- pairs in the event. Search channels binned in ET have been combined into coarse ET bins for the purposes of presentation.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> HIGGS GRAVITINO) = BR(NEUTRALINO1 --> Z GRAVITINO) = 0.5 for strong plus electroweak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> HIGGS GRAVITINO) = 1.0 for strong plus electroweak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0 for strong plus electroweak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> HIGGS GRAVITINO) = BR(NEUTRALINO1 --> Z0 GRAVITINO) = 0.5. The chargino-Higgsino mass is fixed at 150 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> HIGGS GRAVITINO) = BR(NEUTRALINO1 --> Z0 GRAVITINO) = 0.5. The chargino-Higgsino mass is fixed at 350 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> HIGGS GRAVITINO) = 1.0. The chargino-Higgsino mass is fixed at 150 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> HIGGS GRAVITINO) = 1.0. The chargino-Higgsino mass is fixed at 350 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0. The chargino-Higgsino mass is fixed at 150 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the natural Higgsino NLSP scenario with strong plus electroweak production for BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0. The chargino-Higgsino mass is fixed at 350 GeV.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 150 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z GRAVITINO) = 1.0 and strong plus weak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 250 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0 and strong plus weak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limit on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 350 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z0 GRAVITINO) = 1.0 and strong plus weak production.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the slepton co-NLSP model in the gluino-chargino (wino-like chargino) mass plane.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the stau-NLSP and stau-NNLSP scenario in the degenerate smuon- and selectron- stau mass plane.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the T1tttt model in the gluino-neutralino mass plane.

The product of acceptance and efficiency for the calculation of the 95% C.L. limits for the T6ttWW model in the sbottom-chargino mass plane.

Observed 95% C.L. exclusion contour for the T1tttt scenario.

Observed - 1sig(theoretical) 95% C.L. exclusion contour for the T1tttt scenario.

Observed + 1sig(theoretical) 95% C.L. exclusion contour for the T1tttt scenario.

Expected 95% C.L. exclusion contour for the T1tttt scenario.

Expected - 1sig 95% C.L. exclusion contour for the T1tttt scenario.

Expected + 1sig 95% C.L. exclusion contour for the T1tttt scenario.

Observed 95% C.L. exclusion contour for the T6ttWW scenario.

Observed - 1sig(theoretical) 95% C.L. exclusion contour for the T6ttWW scenario.

Observed + 1sig(theoretical) 95% C.L. exclusion contour for the T6ttWW scenario.

Expected 95% C.L. exclusion contour for the T6ttWW scenario.

Expected - 1sig 95% C.L. exclusion contour for the T6ttWW scenario.

Expected + 1sig 95% C.L. exclusion contour for the T6ttWW scenario.

Observed and expected 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> HIGGS GRAVITINO) = BR(NEUTRALINO1 --> Z GRAVITINO) = 0.5 for strong plus electroweak production.

Observed and expected 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> HIGGS GRAVITINO) = 1.0 for strong plus electroweak production. Successive instances of SIG(OBS) = SIG(EXP) =0 have been rebinned over M(STOP) to reduce the size of the table.

Observed and expected 95% C.L. limits for the natural Higgsino NLSP scenario with BR(NEUTRALINO1 --> Z0 GLUINO) = 1.0 for strong plus electroweak production.

Observed 95% C.L. limits on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 150 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z GRAVITINO) = 1.0 and strong plus weak production.

Observed 95% C.L. limits on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 250 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z GRAVITINO) = 1.0 and strong plus weak production.

Observed 95% C.L. limits on BR(NEUTRALINO1 --> HIGGS GRAVITINO) for the natural Higgsino NLSP scenario with fixed chargino-Higgsino mass of 350 GeV assuming BR(NEUTRALINO1 --> HIGGS GRAVITINO) + BR(NEUTRALINO1 --> Z GRAVITINO) = 1.0 and strong plus weak production.

Observed and expected 95% C.L. limits for the slepton co-NLSP model in the gluino-chargino (wino-like chargino) mass plane are shown.

Observed and expected 95% C.L. limits for the stau-NLSP and stau-NNLSP scenario in the degenerate smuon- and selectron- stau mass plane are shown. Successive instances of SIG(OBS) = SIG(EXP) =0 have been rebinned over M(SELECTRON) to reduce the size of the table.

Observed and expected 95% C.L. limits for the T1tttt model in the gluino-neutralino mass plane are shown.

Observed and expected 95% C.L. limits for the T6ttWW model in the sbottom-chargino mass plane are given.