Distribution of jet girth of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of uGNN output score of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of aGNN output score of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Template fit of the DeepJet discriminator used to determine the b jet fraction of the non-EMJ tagged jets in data events that pass the 1-EMJ selection of the event selection criteria defined in the paper.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers u-tag 1, u-tag 2, and u-tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers uGNN tag 1, uGNN tag 2, and uGNN tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers u-tag 4 and u-tag 5, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The expected and observed 95% CL upper limits on the production cross section for various signal models in the unflavored scenario model-generic and GNN EMJ tagging methods.

The expected and observed 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario model-generic and GNN EMJ tagging methods.

The observed yield of events in data satisfying the validation selection criteria with at least two jets passing the corresponding validation tag, and the estimation based on the misidentification rate calculated using validation events with exactly one jet passing the validation tagger scaled by the factor given in Equation (4) of the paper.

The estimated number of events from the background prediction based on control samples in data and the observed event yields.

Distribution of event $H_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of second leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of third leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of forth leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of Track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $D_{N}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of jet $\overline{\tau}_{2/1}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of $|\Delta\eta|$ between jet and tracks of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of $|\Delta\phi|$ between jet and tracks of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{z}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $p_\mathrm{T}$ fraction of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers a-tag 1, a-tag 2, a-tag 3, and a-tag 4, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers aGNN tag 1, aGNN tag 2, and aGNN tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

Signal acceptance of signal models in the unflavored scenario with using the designated cut sets with the model agnostic and GNN EJ tagging methods.

Signal acceptance of signal models in the flavor-aligned scenario with using the designated cut sets with the model agnostic and GNN EJ tagging methods.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Distribution of jet-associated tracks of jets in SM multijet processes that pass the basic kinematic selection, the uGNN tag 1 tagging criteria, and aGNN tag 1 EMJ tagging criteria as a function of $\Delta R$ between the jet and track of interest and $\mathrm{sign}(d_{xy})\cdot\ln\left( 1+\left\lvert\frac{d_{xy}}{1\mathrm{cm}}\right\rvert\right)$, with $d_{xy}$ being the transverse impact parameter of the track of interest.

Example cut flow used for selecting signals events using the u-set 4 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 5 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 4 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 5 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 2 selection criteria. Both background and MC events has been normalized to unity.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The j+b-jet invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The 2b-jet invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The j+e invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The b-jet+e invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The j+$\gamma$ invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The j+$\mu$ invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The b-jet+$\mu$ invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The b-jet+$\gamma$ invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

The 95% CL upper limits on the cross section times acceptance ($A$), efficiency ($\epsilon$), and branching ratio ($B$) for Gaussian-shaped signals with different signal widths. The limits are calculated for events with at least one lepton with pT > 60 GeV in the 10 pb AR. Two width hypotheses are shown; $\sigma X / mX = 0$ and $\sigma X / mX = 0.15$. In both cases, the detector resolution for jets is included in the simulation of signal samples. The $\pm1 \sigma$ and $\pm2 \sigma$ bands around the expected limit are shown for $\sigma X / mX = 0$ signals. Mass points are spaced 5% apart, relative to the preceding point, starting at 0.3 TeV.

Distributions of the anomaly score from the AE for data and five benchmark BSM models. Their legends, from top to bottom, are; (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$, with the $H^{+}$ mass of 2 TeV; (2) a 6 TeV Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a 4 TeV $Z'$ boson decaying to a composite lepton $E$ with a mass of 0.5 TeV; (4) the sequential standard model $W' \rightarrow WZ' \rightarrow \ell\nu q\bar{q}$, with the $W'$ mass of 6.2 TeV and the $Z'$ mass of 6 TeV; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$ with a mass of 6 TeV. The distributions for the BSM models are smoothed to remove fluctuations due to low MC event counts. The vertical lines indicate the start of the three anomaly regions (ARs). The labels of the three ARs indicate the visible cross section for hypothetical processes yielding the same number of events as observed in the 140 $fb^{-1}$ dataset. The AE is applied to preselected events without any requirements on invariant mass distributions.

Invariant mass distributions of $j+Y$ for $M_{jY}$ > 0.3 TeV after preselection along with the fit from Eq.(1). The fit is represented by red lines, and the associated uncertainties are indicated by yellow bands. The lower panels show the bin-by-bin significances of deviations from the fit, calculated as $(d_{\textit{i}} - f_{\textit{i}}) / \delta_{\textit{i}}$, where $d_{\textit{i}}$ is the data yield, $f_{\textit{i}}$ is the fit value, and $\delta_{i}$ is the uncertainty of data in the $\textit{i}$-th bin

Distributions of the anomaly score for data and several anomaly scenarios. The example BSM model (shown with the dashed blue lines) is the sequential standard model $W' \rightarrow WZ' \rightarrow \ell\nu q\bar{q}$. The mass of $W'$ is set to 2.2 TeV and the mass of the $Z'$ is set to 2 TeV. This model leads to the final state of one lepton, two jets, and small missing transverse energy that is similar to the SM backgrounds. The other histograms represent events from the same model with artificial modifications to represent anomalous events; (1) "Anomaly 1" is the case where all jets beyond the second jet ($N_{j}$ > 2) are replaced with photons; (2) "Anomaly 2" is the case where all jets beyond the second jet are replaced with $b$-jets; (3) "Anomaly 3" is the case of low-multiplicity events where all jets beyond the second jet are removed. The histograms are normalized to unit. The left peak near log(Loss) = -9 visible in Anomaly 1 and 2 is from the events without a third jet.

Measured min-$d_{xy}$ distribution in the 2-match category, after requiring the $d_{xy}$ muon to pass the isolation requirement $I_{\mathrm{rel}}^{\mathrm{PF}} <0.25$ (i. e., the B and D bins of the ABCD plane). Overlaid with a red histogram is the background predicted from the region of the ABCD plane failing the same requirement (the A and C bins), as well as three signal benchmark hypotheses (as defined in the legends), assuming $\alpha_D = \alpha_{\mathrm{EM}}$ (the fine-structure constant). The red hatched bands correspond to the background prediction uncertainty. The last bin includes the overflow.

Two-dimensional exclusion surfaces for $\Delta = 0.1 \, m_1$ as a function of the DM mass $m_1$ and the signal strength $y$, with $m_{A'} = 3 \, m_1$. Filled histograms denote observed limits on $\sigma(\mathrm{pp} \to A' \to \chi_2 \chi_1) \, \mathcal{B}(\chi_2 \to \chi_1 \mu^+ \mu^-)$. Solid (dashed) curves denote the observed (expected) exclusion limits at 95% CL, with 68% CL uncertainty bands around the expectation. Regions above the curves are excluded, depending on the $\alpha_D$ hypothesis: $\alpha_{\mathrm{D}} = \alpha_{\mathrm{EM}}$ (dark blue) or 0.1 (light magenta).

Two-dimensional exclusion surfaces for $\Delta = 0.4 \, m_1$ as a function of the DM mass $m_1$ and the signal strength $y$, with $m_{A'} = 3 \, m_1$. Filled histograms denote observed limits on $\sigma(\mathrm{pp} \to A' \to \chi_2 \chi_1) \, \mathcal{B}(\chi_2 \to \chi_1 \mu^+ \mu^-)$. Solid (dashed) curves denote the observed (expected) exclusion limits at 95% CL, with 68% CL uncertainty bands around the expectation. Regions above the curves are excluded, depending on the $\alpha_D$ hypothesis: $\alpha_{\mathrm{D}} = \alpha_{\mathrm{EM}}$ (dark blue) or 0.1 (light magenta).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 0L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 1L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The observed exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Only signals simulating the tW+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_a$ vs. $m_{H^{\pm}}$ and assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 150 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The observed exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

The expected exclusion contour at 95% CL as a function of the $m_{H^{\pm}}$ vs. tan$\beta$ and assuming $m_a$ = 250 $\mathrm{GeV}$, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Masses that are within the contours are excluded. Signals simulating the tW+DM + tt+DM final states are considered in this contour. These exclusion contours are derived using the 2L channel only.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.7$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Only signals simulating the tW+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_a$ vs. $ m_{H^{\pm}}$ signal grid assuming tan$\beta$ = 1, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 150 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

Model dependent upper limit on the cross section for the $m_{H^{\pm}}$ vs. tan$\beta$ signal grid assuming $m_a$ = 250 GeV, $m_{\mathrm{DM}} = 10 \mathrm{GeV}$, $g_{\chi} = 1$ and sin$\theta = 0.35$. Signals simulating the tW+DM + tt+DM final states are considered. Upper limits with large $\mu_{\mathrm{sig}}$ for the observed limit are capped at 500.

The distributions of $m_{\mathrm{b1},\mathrm{W-tagged}}$ in the 0L inclusive signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

The distributions of $m_{\mathrm{T}}^{\mathrm{b,E_{\mathrm{T}^{\mathrm{miss}}}}}$ in the 0L inclusive signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

The distributions of $N_{\mathrm{W-tagged}}$ in the 0L inclusive signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

The distributions of $m_{\mathrm{b1},\mathrm{\cancel{b1}}}$ in the hadronic top inclusive signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

The distributions of $m_{\mathrm{b1},\mathrm{\cancel{b1}}}$ in the leptonic top inclusive signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

The distributions of $m_{\mathrm{b1},\mathrm{\cancel{b1}}}$ in the leptonic top inclusive signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Cutflow for the reference point $(\it{m}_{\mathrm{H^{\pm}}}, \it{m}_{a}, tan\beta, sin\theta )=$ (500,100,1,0.7) , (800,150,20,0.7), (600,250,30,0.7), (1000,400,1,0.7) in 0L regions. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(\it{m}_{\mathrm{H^{\pm}}}, \it{m}_{a}, tan\beta, sin\theta )=$ (500,100,1,0.7) , (800,150,20,0.7), (600,250,30,0.7), (1000,400,1,0.7) in 1L leptonic top regions. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(\it{m}_{\mathrm{H^{\pm}}}, \it{m}_{a}, tan\beta, sin\theta )=$ (500,100,1,0.7) , (800,150,20,0.7), (600,250,30,0.7), (1000,400,1,0.7) in 1L hadronic top regions. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Signal acceptance in the 0L region for 2HDM+a model DM signals on the plane defined by m$_a$--m$_{H^{\pm}}$ assuming tan$\beta$ = 1, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 0L region for 2HDM+a model DM signals on the plane defined by m$_a$--m$_{H^{\pm}}$ assuming tan$\beta$ = 1, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 0L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 150 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 0L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 150 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 0L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 250 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 0L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 250 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 1L region for 2HDM+a model DM signals on the plane defined by m$_a$--m$_{H^{\pm}}$ assuming tan$\beta$ = 1, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 1L region for 2HDM+a model DM signals on the plane defined by m$_a$--m$_{H^{\pm}}$ assuming tan$\beta$ = 1, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 1L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 150 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 1L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 150 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 1L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 250 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 1L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 250 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.7. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 0L region for 2HDM+a model DM signals on the plane defined by m$_a$--m$_{H^{\pm}}$ assuming tan$\beta$ = 1, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 0L region for 2HDM+a model DM signals on the plane defined by m$_a$--m$_{H^{\pm}}$ assuming tan$\beta$ = 1, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 0L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 150 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 0L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 150 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 0L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 250 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 0L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 250 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 1L region for 2HDM+a model DM signals on the plane defined by m$_a$--m$_{H^{\pm}}$ assuming tan$\beta$ = 1, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 1L region for 2HDM+a model DM signals on the plane defined by m$_a$--m$_{H^{\pm}}$ assuming tan$\beta$ = 1, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 1L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 150 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 1L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 150 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

Signal acceptance in the 1L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 250 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in the 1L region for 2HDM+a model DM signals on the plane defined by m$_{H^{\pm}}$--tan$\beta$ assuming m$_a$ = 250 GeV, m$_{\chi}$= 10 GeV and sin$\theta$ = 0.35. Please mind that the efficiency given in the table is multiplied by factor of $10^{2}$

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.

The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $p_T^{miss}$ after scaling the simulation to match the total yield in data.

Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_t$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.

The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_t$ after scaling the simulation to match the total yield in data.

Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_W$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.

The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_W$ after scaling the simulation to match the total yield in data.

Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_{\text{res}}$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.

The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_{\text{res}}$ after scaling the simulation to match the total yield in data.

Observed event yields in data (black points) and predicted SM background (filled histograms) for the low $\Delta$m search bins 0--52. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.

The ratio of the data to the total background prediction for the low $\Delta$m search bins 0--52. The hatched bands correspond to the total uncertainty in the background prediction.

Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 53--104. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.

The ratio of the data to the total background prediction for the high $\Delta$m search bins 53--104. The hatched bands correspond to the total uncertainty in the background prediction.

Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 105--152 with ${N_b = 2}$. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.

The ratio of the data to the total background prediction for the high $\Delta$m search bins 105--152 with ${N_b = 2}$. The hatched bands correspond to the total uncertainty in the background prediction.

Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 153--182 with ${N_b \geq 3}$. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.

The ratio of the data to the total background prediction for the high $\Delta$m search bins 153--182 with ${N_b \geq 3}$. The hatched bands correspond to the total uncertainty in the background prediction.

The observed 95% CL upper limit on the production cross section of the T2tt simplified model as a function of the top squark and LSP masses. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.

The expected 95% CL upper limit on the production cross section of the T2tt simplified model as a function of the top squark and LSP masses. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.

The observed exclusion contour of the T2tt simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$). No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.

The mean expected exclusion contour of the T2tt simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.

The observed 95% CL upper limit on the production cross section of the T2bW simplified model as a function of the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2bW simplified model as a function of the top squark and LSP masses.

The observed exclusion contour of the T2bW simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2bW simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T2tb simplified model as a function of the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2tb simplified model as a function of the top squark and LSP masses.

The observed exclusion contour of the T2tb simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2tb simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T2ttC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2ttC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The observed exclusion contour of the T2ttC simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2ttC simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T2bWC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2bWC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The observed exclusion contour of the T2bWC simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2bWC simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T2cc simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2cc simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The observed exclusion contour of the T2cc simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2cc simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T1tttt simplified model as a function of the gluino and LSP masses.

The expected 95% CL upper limit on the production cross section of the T1tttt simplified model as a function of the gluino and LSP masses.

The observed exclusion contour of the T1tttt simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T1tttt simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T1ttbb simplified model as a function of the gluino and LSP masses.

The expected 95% CL upper limit on the production cross section of the T1ttbb simplified model as a function of the gluino and LSP masses.

The observed exclusion contour of the T1ttbb simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T1ttbb simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T5ttcc simplified model as a function of the gluino and LSP masses. The upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.

The expected 95% CL upper limit on the production cross section of the T5ttcc simplified model as a function of the gluino and LSP masses. The uppser limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.

The observed exclusion contour of the T5ttcc simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$). The expected and observed upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.

The mean expected exclusion contour of the T5ttcc simplified model and the region containing 68% and 95% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis. The expected and observed upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.

The ratios (&mu;) of the 95&#37; C.L. upper limits on the combined s&rarr; W^&plusmn;W^&#8723; and s&rarr; ZZ cross section to simplified model expectations for the m_Z'=1 TeV scenario, for various m_s hypotheses. The observed limits (solid line) are consistent with the expectation under the SM-only hypothesis (dashed line) within uncertainties (filled band), except for a small excess for m_s=160 GeV, discussed in the text.

The ratios (&mu;) of the 95&#37; C.L. upper limits on the combined s&rarr; W^&plusmn;W^&#8723; and s&rarr; ZZ cross section to simplified model expectations for the m_Z'=1.7 TeV scenario, for various m_s hypotheses. The observed limits (solid line) are consistent with the expectation under the SM-only hypothesis (dashed line) within uncertainties (filled band), except for a small excess for m_s=160 GeV, discussed in the text.

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

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

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

SM background post-fit yields stacked in each SR and CR category and E_T^miss bin and data overlaid with the maximum likelihood estimators set to the conditional values of the combined signal and control region fit. The hatched uncertainty band shown includes simulation statistics uncertainties, experimental systematic uncertainties, and V+jets theory modelling systematic uncertainties. Pre-fit uncertainties cover differences between the data and pre-fit background prediction.

Cumulative efficiencies for the merged category for signal samples with m_s=160 GeV (a), m_s=235 GeV (b) and m_s=310 GeV (c), each with m_Z'=1 TeV. The dark Higgs candidate selection includes stringent jet substructure requirements and typically at most one candidate is present in signal events. Here, &Delta; &phi;_{jets_1,2,3 E_T^miss} is the smallest azimuthal angle between the E_T^miss and any of the three highest-p_T (leading) small-R jets.

Cumulative efficiencies for the intermediate category for signal samples with m_s=160 GeV (a), m_s=235 GeV (b) and m_s=310 GeV (c), each with m_Z'=1 TeV. The TAR+Comb algorithm reconstructs the dark Higgs candidate from a TAR jet with m^TAR&gt;60 GeV that is supplemented by up to two additional small-R jets within &Delta;R_cone=2.5 of the TAR jet. Here, &Delta; &phi;_{jets_1,2,3 E_T^miss} is the smallest azimuthal angle between the E_T^miss and any of the three highest-p_T (leading) small-R jets. For details see text.

The product of acceptance and efficiency (A &times; &varepsilon;), defined as the number of signal events satisfying the full set of selection criteria in the merged or intermediate signal regions, divided by the total number of generated signal events, for the s(W^&plusmn;W^&#8723;) dark Higgs signal points with dark Higgs boson mass m_s and Z' boson mass m_Z'.

The product of acceptance and efficiency (A &times; &varepsilon;), defined as the number of signal events satisfying the full set of selection criteria in the merged or intermediate signal regions, divided by the total number of generated signal events, for the s(ZZ) dark Higgs signal points with dark Higgs boson mass m_s and Z' boson mass m_Z'.

The timing distribution of the background sources predicted to contribute to the signal region, compared to those for a representative signal model. The time is defined by the jet in the event with the largest $t_{\mathrm{jet}}$ passing the relevant selection. The distributions for the major backgrounds are taken from control regions and normalized to the predictions. The observed data is shown by the black points. No events are observed in data for $t_{\mathrm{jet}} > 3\,$ns (indicated with a vertical black line).

The product of the acceptance and efficiency in the $c\tau_{0}$ vs. $m_{\tilde{g}}$ plane for the GMSB model, after all requirements.

The product of the acceptance and efficiency in the $c\tau_{0}$ vs. $m_{\tilde{g}}$ plane for the GMSB model, after all requirements.

The observed upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed.

The observed upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The observed upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed.

The observed upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The observed and expected upper limits at $95\%$ CL on the gluino pair production cross section for a gluino GMSB model with $m_{\tilde{g}}=2400$ GeV. The one (two) standard deviation variation in the expected limit is shown in the inner green (outer yellow) band. The blue solid line shows the observed limit obtained by the CMS displaced jet search

The observed and expected upper limits at $95\%$ CL on the gluino pair production cross section for a gluino GMSB model with $m_{\tilde{g}}=2400$ GeV. The one (two) standard deviation variation in the expected limit is shown in the inner green (outer yellow) band. The blue solid line shows the observed limit obtained by the CMS displaced jet search

The distribution (normalized to unity) of number of ECAL cells hit in the jet for jets in a background enriched data sample (satisfying $|\eta| < 1.48$, $PV_{\rm track}^{\rm fraction} > 1/12$, $\mathrm{HEF} > 0.2$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$ and $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$) and for signal jets satisfying signal region requirements (except those on $E_{\mathrm{ECAL}}$ and $N^{\mathrm{cell}}_{\mathrm{ECAL}}$).

The distribution (normalized to unity) of number of ECAL cells hit in the jet for jets in a background enriched data sample (satisfying $|\eta| < 1.48$, $p_\mathrm{T}tf > 0.08$, $\mathrm{HEF} > 0.2$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$ and $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$) and for signal jets satisfying signal region requirements (except those on $E_{\mathrm{ECAL}}$ and $N^{\mathrm{cell}}_{\mathrm{ECAL}}$).

The distribution (normalized to unity) of $\mathrm{HEF}$ for a data sample enriched in beam halo and noise jets (satisfying $|\eta| < 1.48$, $p_\mathrm{T} > 30 \,\mathrm{GeV}$, $PV_{\rm track}^{\rm fraction} < 1/12$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$ and $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$) and for signal jets passing signal region selections (except on $\mathrm{HEF}$).

The distribution (normalized to unity) of $\mathrm{HEF}$ for a data sample enriched in beam halo and noise jets (satisfying $|\eta| < 1.48$, $p_\mathrm{T} > 30 \,\mathrm{GeV}$, $p_\mathrm{T}tf < 0.08$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$ and $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$) and for signal jets passing signal region selections (except on $\mathrm{HEF}$).

The distribution (normalized to unity) of $E_{\mathrm{HCAL}}$ for a data sample enriched in beam halo and noise jets (satisfying $|\eta| < 1.48$, $p_\mathrm{T} > 30 \,\mathrm{GeV}$, $PV_{\rm track}^{\rm fraction} < 1/12$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$ and $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$) and for signal jets passing signal region selections (except on $E_{\mathrm{HCAL}}$).

The distribution (normalized to unity) of $E_{\mathrm{HCAL}}$ for a data sample enriched in beam halo and noise jets (satisfying $|\eta| < 1.48$, $p_\mathrm{T} > 30 \,\mathrm{GeV}$, $p_\mathrm{T}tf < 0.08$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$ and $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$) and for signal jets passing signal region selections (except on $E_{\mathrm{HCAL}}$).

The distribution (normalized to unity) of $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}}$ for data sample enriched in jets from noise (satisfying $|\eta| < 1.48$, $p_\mathrm{T} > 30\,\mathrm{GeV}$, $PV_{\rm track}^{\rm fraction} > 1/12$, $\mathrm{HEF} > 0.2$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20 \,\mathrm{GeV}$ and $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$) and for signal jets passing signal region selections (except on $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}}$ and $t^{\mathrm{RMS}}_\mathrm{jet}$)

The distribution (normalized to unity) of $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}}$ for data sample enriched in jets from noise (satisfying $|\eta| < 1.48$, $p_\mathrm{T} > 30\,\mathrm{GeV}$, $p_\mathrm{T}tf > 0.08$, $\mathrm{HEF} > 0.2$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20 \,\mathrm{GeV}$ and $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$) and for signal jets passing signal region selections (except on $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}}$ and $t^{\mathrm{RMS}}_\mathrm{jet}$)

The distribution (normalized to unity) of $t^{\mathrm{RMS}}_\mathrm{jet}$ for data sample enriched in jets from noise (satisfying $|\eta| < 1.48$, $p_\mathrm{T} > 30\,\mathrm{GeV}$, $PV_{\rm track}^{\rm fraction} > 1/12$, $\mathrm{HEF} > 0.2$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20 \,\mathrm{GeV}$ and $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$) and for signal jets passing signal region selections (except on $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}}$ and $t^{\mathrm{RMS}}_\mathrm{jet}$)

The distribution (normalized to unity) of $t^{\mathrm{RMS}}_\mathrm{jet}$ for data sample enriched in jets from noise (satisfying $|\eta| < 1.48$, $p_\mathrm{T} > 30\,\mathrm{GeV}$, $p_\mathrm{T}tf > 0.08$, $\mathrm{HEF} > 0.2$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20 \,\mathrm{GeV}$ and $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$) and for signal jets passing signal region selections (except on $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}}$ and $t^{\mathrm{RMS}}_\mathrm{jet}$)

The distribution (normalized to unity) of $PV_{\rm track}^{\rm fraction}$ for a data sample enriched in main bunch backgrounds (satisfying $p_\mathrm{T} > 30\,\mathrm{GeV}$, $|\eta| < 1.48$, $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$, $|t_{\mathrm{jet}}| < 3\,\mathrm{ns}$ and $E_{\mathrm{ECAL}} >20\,\mathrm{GeV}$) and for signal jets passing signal selections (except on $PV_{\rm track}^{\rm fraction}$).

The distribution (normalized to unity) of $p_\mathrm{T}tf$ for a data sample enriched in main bunch backgrounds (satisfying $p_\mathrm{T} > 30\,\mathrm{GeV}$, $|\eta| < 1.48$, $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$, $|t_{\mathrm{jet}}| < 3\,\mathrm{ns}$ and $E_{\mathrm{ECAL}} >20\,\mathrm{GeV}$) and for signal jets passing signal selections (except on $p_\mathrm{T}tf$).

The distribution (normalized to unity) of $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}}$ for a data sample enriched in beam halo (satisfying $p_\mathrm{T} > 30\,\mathrm{GeV}$, $|\eta| < 1.48$, $PV_{\rm track}^{\rm fraction} < 1/12$, $\mathrm{HEF} < 0.2$, $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$ and $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$) and for signal jets passing signal selections (except on $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}}$)

The distribution (normalized to unity) of $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}}$ for a data sample enriched in beam halo (satisfying $p_\mathrm{T} > 30\,\mathrm{GeV}$, $|\eta| < 1.48$, $p_\mathrm{T}tf < 0.08$, $\mathrm{HEF} < 0.2$, $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$, $t_{\mathrm{jet}} < -3\,\mathrm{ns}$ and $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$) and for signal jets passing signal selections (except on $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}}$)

The distribution (normalized to unity) of $\mathrm{max}(\Delta \phi_{\mathrm{DT}})$ for a data sample enriched in cosmic muons (satisfying $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$, $p_\mathrm{T} > 30\,\mathrm{GeV}$, $|\eta| < 1.48$, $PV_{\rm track}^{\rm fraction} < 1/12$, $\mathrm{HEF} > 0.2$, $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$, $t_{\mathrm{jet}} > 3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$ and failing the HCAL noise rejection quality filters) and for signal jets passing signal selections (except on $\mathrm{max}(\Delta \phi_{\mathrm{DT}})$).

The distribution (normalized to unity) of $\mathrm{max}(\Delta \phi_{\mathrm{DT}})$ for a data sample enriched in cosmic muons (satisfying $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$, $p_\mathrm{T} > 30\,\mathrm{GeV}$, $|\eta| < 1.48$, $p_\mathrm{T}tf < 0.08$, $\mathrm{HEF} > 0.2$, $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$, $t_{\mathrm{jet}} > 3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$ and failing the HCAL noise rejection quality filters) and for signal jets passing signal selections (except on $\mathrm{max}(\Delta \phi_{\mathrm{DT}})$).

The distribution (normalized to unity) of $\mathrm{max}(\Delta \phi_{\mathrm{RPC}})$ for a data sample enriched in cosmic muons (satisfying $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$, $p_\mathrm{T} > 30\,\mathrm{GeV}$, $|\eta| < 1.48$, $PV_{\rm track}^{\rm fraction} < 1/12$, $\mathrm{HEF} > 0.2$, $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$, $t_{\mathrm{jet}} > 3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$ and failing the HCAL noise rejection quality filters) and for signal jets passing signal selections (except on $\mathrm{max}(\Delta \phi_{\mathrm{RPC}})$).

The distribution (normalized to unity) of $\mathrm{max}(\Delta \phi_{\mathrm{RPC}})$ for a data sample enriched in cosmic muons (satisfying $E^{\mathrm{CSC}}_\mathrm{ECAL}/E_{\mathrm{ECAL}} < 0.8$, $p_\mathrm{T} > 30\,\mathrm{GeV}$, $|\eta| < 1.48$, $p_\mathrm{T}tf < 0.08$, $\mathrm{HEF} > 0.2$, $t^{\mathrm{RMS}}_\mathrm{jet}/t_{\mathrm{jet}} < 0.4$, $t_{\mathrm{jet}} > 3\,\mathrm{ns}$, $E_{\mathrm{ECAL}} > 20\,\mathrm{GeV}$ and failing the HCAL noise rejection quality filters) and for signal jets passing signal selections (except on $\mathrm{max}(\Delta \phi_{\mathrm{RPC}})$).

Distribution of $t_{\mathrm{jet}}$ for jets with the full Run 2 dataset with no cleaning selection applied (a) and after all jet cleaning selections are applied (b) in events satisfying the trigger requirements and satisfying $p_T^\mathrm{miss} > 300$. The jets are required to pass an inverted selection of $PV_{\rm track}^{\rm fraction} > 1/12$ to enrich the sample in those originating from main and satellite bunch backgrounds. The cleaning selections are shown to reduce the backgrounds by many orders of magnitude.

Distribution of $t_{\mathrm{jet}}$ for jets with the full Run 2 dataset with no cleaning selection applied (a) and after all jet cleaning selections are applied (b) in events satisfying the trigger requirements and satisfying $p_T^\mathrm{miss} > 300$. The jets are required to pass an inverted selection of $PV_{\rm track}^{\rm fraction} > 0.08$ to enrich the sample in those originating from main and satellite bunch backgrounds. The cleaning selections are shown to reduce the backgrounds by many orders of magnitude.

Distribution of $t_{\mathrm{jet}}$ for jets with the full Run 2 dataset in events satisfying the trigger requirements and satisfying $p_T^\mathrm{miss} < 300$. The jets are required to pass an inverted selection of $PV_{\rm track}^{\rm fraction} > 1/12$ to enrich the sample in those originating from main and satellite bunch backgrounds (all other jet cleaning selections are applied). Clear contributions from jets from satellite bunch collisions can be seen peaked around -5, 5 and 10 ns.

Distribution of $t_{\mathrm{jet}}$ for jets with the full Run 2 dataset in events satisfying the trigger requirements and satisfying $p_T^\mathrm{miss} < 300$. The jets are required to pass an inverted selection of $PV_{\rm track}^{\rm fraction} > 0.08$ to enrich the sample in those originating from main and satellite bunch backgrounds (all other jet cleaning selections are applied). Clear contributions from jets from satellite bunch collisions can be seen peaked around -5, 5 and 10 ns.

Contribution to the delayed time of jets from the $\beta$ of the gluino is plotted against the delay contribution from the difference (assuming straight line paths) between the path taken by the gluino and the particle forming the jet from the path length for a particle travelling directly to the same position on the ECAL barrel for gluino $c\tau_{0} = 10$ m and mass of 1000 Gev (top) and 3000 GeV (bottom). The dominant contribution is shown to be the gluino $\beta$.

Contribution to the delayed time of jets from the $\beta$ of the gluino is plotted against the delay contribution from the difference (assuming straight line paths) between the path taken by the gluino and the particle forming the jet from the path length for a particle travelling directly to the same position on the ECAL barrel for gluino $c\tau_{0} = 10$ m and mass of 1000 Gev (top) and 3000 GeV (bottom). The dominant contribution is shown to be the gluino $\beta$.

Selection efficiencies for the GMSB model with $m_{\tilde{g}}=1000$ and various proper decay lengths

Selection efficiencies for the GMSB model with $m_{\tilde{g}}=1000$ and various proper decay lengths

Selection efficiencies for the GMSB model with $m_{\tilde{g}}=2400$ and various proper decay lengths

Selection efficiencies for the GMSB model with $m_{\tilde{g}}=2400$ and various proper decay lengths

Selection efficiencies for the GMSB model with $m_{\tilde{g}}=3000$ and various proper decay lengths

Selection efficiencies for the GMSB model with $m_{\tilde{g}}=3000$ and various proper decay lengths

The observed upper limits at $95\%$ CL for the gluino pair production cross section relative to the theoretical cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The observed upper limits at $95\%$ CL for the gluino pair production cross section relative to the theoretical cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected upper limits at $95\%$ CL for the gluino pair production cross section relative to the theoretical cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected upper limits at $95\%$ CL for the gluino pair production cross section relative to the theoretical cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected plus 1 $\sigma_{\mathrm{experiment}}$ upper limits at $95\%$ CL for the gluino pair production cross section relative to the theoretical cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected plus 1 $\sigma_{\mathrm{experiment}}$ upper limits at $95\%$ CL for the gluino pair production cross section relative to the theoretical cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected minus 1 $\sigma_{\mathrm{experiment}}$ upper limits at $95\%$ CL for the gluino pair production cross section relative to the theoretical cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected minus 1 $\sigma_{\mathrm{experiment}}$ upper limits at $95\%$ CL for the gluino pair production cross section relative to the theoretical cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected plus 1 $\sigma_{\mathrm{experiment}}$ upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected plus 1 $\sigma_{\mathrm{experiment}}$ upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected minus 1 $\sigma_{\mathrm{experiment}}$ upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

The expected minus 1 $\sigma_{\mathrm{experiment}}$ upper limits at $95\%$ CL for the gluino pair production cross section in the GMSB model, shown in the plane of $m_{\tilde{g}}$ and $c\tau_{0}$. A branching fraction of $100\%$ for the gluino decay to a gluon and a gravitino is assumed. The area below the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their $\pm 1$ standard deviation ranges from experimental uncertainties. The thin black lines show the effect of the theoretical uncertainties on the signal cross section.

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

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

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

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

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

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

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