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).

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 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.

Figure 3 (upper). Distribution of $M_{T2}(ll)$ in a control region enriched in $t\bar{t}$ events defined by requiring at least 2 jets, at least one of them b-tagged, and missing transverse momentum below 80 GeV. For the plot, simulated yields are normalized to data using the yields in the $M_{T2}(ll)$ < 100 GeV region.

Figure 4 (upper). Expected and observed yields in the five $t\bar{t}Z$ control regions, which are defined by different requirements on the number of reconstructed jets and b jets, before the fit.

Figure 4 (lower). Expected and observed yields in the five $t\bar{t}Z$ control regions, which are defined by different requirements on the number of reconstructed jets and b jets, after the fit.

Figure 5 (left). Distribution of $M_{T2}(ll)$ of SF events falling within in the Z boson mass window with at least two jets and no b jet, $p_{T}^{miss} > 80$ GeV and $M_{T2}(ll) > 100$ GeV.

Figure 5 (center). Distribution of $M_{T2}(blbl)$ of SF events falling within in the Z boson mass window with at least two jets and no b jet, $p_{T}^{miss} > 80$ GeV and $M_{T2}(ll) > 100$ GeV.

Figure 5 (right). Distribution of $p_{T}^{miss}$ of SF events falling within in the Z boson mass window with at least two jets and no b jet, $p_{T}^{miss} > 80$ GeV and $M_{T2}(ll) > 100$ GeV.

Figure 6. Event yields in the 13 Drell-Yan and multiboson control regions for events with SF leptons falling within the Z boson mass window and no b jets, after renormalizing with the scale factors obtained from the fit procedure described in the text.

Figure 7 (left). Distributions of $M_{T2}(ll)$ for observed events in the dimuon channel compared to the predicted SM background.

Figure 7 (center). Distributions of $M_{T2}(ll)$ for observed events in the dielectron channel compared to the predicted SM background.

Figure 7 (right). Distributions of $M_{T2}(ll)$ for observed events in the electron-muon channel compared to the predicted SM background.

Figure 8 (left). Distributions of $M_{T2}(ll)$ for all lepton flavors for the defined selection.

Figure 8 (center). Distributions of $M_{T2}(blbl)$ for all lepton flavors for the defined selection, after requiring $M_{T2}(ll) > 100$ GeV.

Figure 8 (right). Distributions of $p_{T}^{miss}$ for all lepton flavors for the defined selection, after requiring $M_{T2}(ll) > 100$ GeV.

Figure 9 (upper). Predicted backgrounds and observed yields in the dielectron and dimuon search regions. The yields are post fit and correspond to the numbers quoted in Tab. 4 of the publication.

Figure 9 (lower). Predicted backgrounds and observed yields in the electron-muon search regions. The yields are post fit and correspond to the numbers quoted in Tab. 4 of the publication.

Figure 10. Predicted backgrounds and observed yields in all search regions combined. The yields are post fit and correspond to the numbers quoted in Tab. 4 of the publication.

Figure 11 (upper). Observed limits for the T2tt model in the top squark - LSP mass plane. The numbers indicate the 95% CL upper limit on the cross section times the square of the branching fraction at each point of the plane.

Figure 11 (upper). Observed exclusion region at 95% CL assuming 100% branching fraction.

Figure 11 (upper). Expected exclusion region at 95% CL assuming 100% branching fraction.

Figure 11 (lower). Observed limits for the T2bW model in the top squark - LSP mass plane. The numbers indicate the 95% CL upper limit on the cross section times the square of the branching fraction at each point of the plane.

Figure 11 (lower). Observed exclusion region at 95% CL assuming 100% branching fraction.

Figure 11 (lower). Expected exclusion region at 95% CL assuming 100% branching fraction.

Figure 12 (upper left). Observed limits for the T8bbllnunu model in the top squark - LSP mass plane. The numbers indicate the 95% CL upper limit on the cross section times the square of the branching fraction at each point of the plane. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.05 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 12 (upper left). Observed exclusion region at 95% CL assuming 100% branching fraction. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.05 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 12 (upper left). Expected exclusion region at 95% CL assuming 100% branching fraction. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.05 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 12 (upper left). Observed limits for the T8bbllnunu model in the top squark - LSP mass plane. The numbers indicate the 95% CL upper limit on the cross section times the square of the branching fraction at each point of the plane. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.5 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 12 (upper left). Observed exclusion region at 95% CL assuming 100% branching fraction. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.5 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 12 (upper left). Expected exclusion region at 95% CL assuming 100% branching fraction. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.5 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 12 (lower). Observed limits for the T8bbllnunu model in the top squark - LSP mass plane. The numbers indicate the 95% CL upper limit on the cross section times the square of the branching fraction at each point of the plane. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.95 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 12 (lower). Observed exclusion region at 95% CL assuming 100% branching fraction. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.95 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 12 (lower). Expected exclusion region at 95% CL assuming 100% branching fraction. $m_{\tilde{\chi}^{\pm}_{1}} = 0.5 (m_{\tilde{t}_{1}} + m_{\tilde{\chi}^{0}_{1}})$, $m_{\tilde{l}} = 0.95 (m_{\tilde{\chi}^{\pm}_{1}} - m_{\tilde{\chi}^{0}_{1}}) + m_{\tilde{\chi}^{0}_{1}}$

Figure 13. Observed limits for the T2tt model in the top squark - LSP mass plane combining the dilepton final state with single-lepton and the all-hadronic final states. The numbers indicate the 95% CL upper limit on the cross section times the square of the branching fraction at each point of the plane.

Figure 13. Observed exclusion region at 95% CL assuming 100% branching fraction.

Figure 13. Expected exclusion region at 95% CL assuming 100% branching fraction.

Figure 14. Observed limits for the T2bW model in the top squark - LSP mass plane combining the dilepton final state with single-lepton and the all-hadronic final states. The numbers indicate the 95% CL upper limit on the cross section times the square of the branching fraction at each point of the plane.

Figure 14. Observed exclusion region at 95% CL assuming 100% branching fraction.

Figure 14. Expected exclusion region at 95% CL assuming 100% branching fraction.

Additional Figure 3. Observed limits for the T2tt model in the top squark - LSP mass plane using aggregate signal regions. The numbers indicate the 95% CL upper limit on the cross section times the square of the branching fraction at each point of the plane.

Additional Figure 3. Observed exclusion region at 95% CL assuming 100% branching fraction.

Additional Figure 3. Expected exclusion region at 95% CL assuming 100% branching fraction.

Table 5. Ratios of $\mu=\frac{\sigma$}{\sigma_{theory}}$ of the 95% CL expected and observed limits to simplified model expectations for different DM particle masses and mediator masses.

Table 6. Expected and observed event yields, summed over all lepton flavors, in aggregate signal regions.

Table 7 (upper). Covariance matrix for the background prediction in aggregate signal regions.

Table 7 (lower). Correlation matrix for the background prediction in aggregate signal regions.

Additional Figure 1. Correlation matrix of the 13 signal regions, split into same-flavor (SF) and different-flavor (DF) regions.

Additional Figure 2. Covariance matrix of the 13 signal regions, split into same-flavor (SF) and different-flavor (DF) regions.

Figure 9a (upper left). The 95% CL upper limit on the production cross section of the T1tttt simplified models as a function of the gluino and LSP masses.

Figure 9a (upper left). Observed exclusion region at 95% CL assuming 100% branching fraction.

Figure 9a (upper left). Expected exclusion region at 95% CL assuming 100% branching fraction.

Figure 9b (upper right). The 95% CL upper limit on the production cross section of the T1ttbb simplified models as a function of the gluino and LSP masses.

Figure 9b (upper right). Observed exclusion region at 95% CL.

Figure 9b (upper right). Expected exclusion region at 95%.

Figure 9c (bottom left). The 95% CL upper limit on the production cross section of the T5tttt simplified models as a function of the gluino and LSP masses. imits are not given for the T5tttt model for mχ~01< 50 GeV for the reason stated in the text.

Figure 9c (bottom left). Observed exclusion region at 95% CL assuming 100% branching fraction.

Figure 9c (bottom left). Expected exclusion region at 95% CL assuming 100% branching fraction.

Figure 9d (bottom right). The 95% CL upper limit on the production cross section of the T5ttcc simplified models as a function of the gluino and LSP masses.

Figure 9d (bottom right). Observed exclusion region at 95% CL.

Figure 9d (bottom right). Expected exclusion region at 95% CL.

The observed number of events and the total background prediction for search regions with Nt = 1 and Nb = 1.

The observed number of events and the total background prediction for search regions with Nt = 1 and Nb ≥ 2.

The observed number of events and the total background prediction for search regions with Nt ≥ 2.

Selected T2tt signal yields for the aggregate search bins.

Selected gluino mediated signal yields for the aggregate search bins.

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.