Observed and expected 95% CL upper limits on the visible $\tau\nu$ production cross section, $\sigma_{\rm vis} = \sigma(pp \to \tau\nu +X) \cdot \mathcal{A} \cdot \varepsilon$, for $m_{\rm T}$ thresholds ranging from 200 to 2950 GeV. See HepData abstract for details on how to use this data for reinterpretation.

Observed 95% CL upper limits on the cross-section times branching ratio as a function of the W′ mass in several NUGIM models defined by the value of the parameter cot$\theta$.

Expected 95% CL upper limits on the cross-section times branching ratio as a function of the W′ mass in several NUGIM models defined by the value of the parameter cot$\theta$.

Acceptance for $W^{\prime}_{\rm SSM}$ as a function of the $W^{\prime}_{\rm SSM}$ mass, shown after successively applying selection at generator-level. The acceptance times efficiency is calculated with respect to all $W^{\prime}_{\rm SSM} \to \tau\nu$ events with a generated $\tau\nu$ mass above 25 GeV. The "selected tau" criteria include the requirement of a $\tau_{\rm had-vis}$ with $p_{\rm T}$ > 30 GeV and $|\eta|$ < 2.4.

Acceptance times efficiency for $W^{\prime}_{\rm SSM}$ as a function of the $W^{\prime}_{\rm SSM}$ mass, shown after successively applying selection at reconstruction-level. The acceptance times efficiency is calculated with respect to all $W^{\prime}_{\rm SSM} \to \tau\nu$ events with a generated $\tau\nu$ mass above 25 GeV. "Preselection" includes all criteria prior to those shown.

Distribution of the NN output in the $\geq$1fj$\,$SR in data and the expected contribution of the signal and background processes after the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions considering the correlations of the uncertainties as obtained by the fit.

Distribution of the NN output in the $t\bar{t}\gamma\,$CR in data and the expected contribution of the signal and background processes after the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions considering the correlations of the uncertainties as obtained by the fit.

Total event yield in the $W\gamma\,$CR in data and the expected contribution of the signal and background processes after the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions considering the correlations of the uncertainties as obtained by the fit.

Distribution of the scalar sum of the jet transverse momenta in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions. The first and last bins include the underflow and overflow, respectively.

Distribution of the $\eta$ of the $b$-tagged jet in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions.

Distribution of the reconstructed top-quark mass in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions. The first and last bins include the underflow and overflow, respectively.

Distribution of the $p_{\mathrm{T}}$ of the top-quark+photon system in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the photon $p_{\mathrm{T}}$ in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the photon $\eta$ in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions.

Distribution of the scalar sum of the jet transverse momenta in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the invariant mass of the $b$-tagged jet and the highest-$p_{\mathrm{T}}$ forward jet in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of $p_{\mathrm{T}}$ of the highest-$p_{\mathrm{T}}$ forward jet in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the difference in $\eta$ between the highest-$p_{\mathrm{T}}$ forward jet and the photon in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the energy of the system formed by the highest-$p_{\mathrm{T}}$ forward jet and the photon in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions. The first and last bins include the underflow and overflow, respectively.

Distribution of the $\eta$ of the $b$-tagged jet in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions.

Distribution of the reconstructed top-quark mass in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions. The first and last bins include the underflow and overflow, respectively.

Distribution of the $p_{\mathrm{T}}$ of the top-quark and photon system in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the photon $p_{\mathrm{T}}$ in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the photon $\eta$ in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions.

Ordered list of the 30 systematic uncertainties with the largest impact on the measured signal normalisation in the fit to data in the parton-level measurement considered as nuisance parameters (NPs) in the profile-likelihood fit. The column "NP value, error" corresponds to the nominal best-fit values and the corresponding uncertainties. The impact of each NP, $\Delta\sigma$/$\sigma_{\mathrm{pred}}$, is computed by comparing the nominal best-fit value of the POI ($\sigma$/$\sigma_{\mathrm{pred}}$) with the result of the fits when fixing the considered NP to its best-fit value shifted by its pre-fit and post-fit uncertainties. The corresponding impacts are listed in the "POI impact prefit high/low" and "POI impact high/low" columns, respectively. The "MC stat." NPs represent the MC statistical uncertainty and they enter the likelihood with a Poisson term, while all the other NPs enter the likelihood via a Gaussian term.

Ordered list of the 30 systematic uncertainties with the largest impact on the measured signal normalisation in the fit to data in the particle-level measurement considered as nuisance parameters (NPs) in the profile-likelihood fit. The column "NP value, error" corresponds to the nominal best-fit values and the corresponding uncertainties. The impact of each NP, $\Delta\sigma$/$\sigma_{\mathrm{pred}}$, is computed by comparing the nominal best-fit value of the POI ($\sigma$/$\sigma_{\mathrm{pred}}$) with the result of the fits when fixing the considered NP to its best-fit value shifted by its pre-fit and post-fit uncertainties. The corresponding impacts are listed in the "POI impact prefit high/low" and "POI impact high/low" columns, respectively. The "MC stat." NPs represent the MC statistical uncertainty and they enter the likelihood with a Poisson term, while all the other NPs enter the likelihood via a Gaussian term.

This table lists the kinematic requirements on parton-level objects used to define of the fiducial phase space for the parton-level measurement. Frixione isolation ($\href{https://arxiv.org/abs/hep-ph/9801442}{\text{hep-ph/9801442}}$) with a chosen radius of $\Delta R = 0.2$ is applied to photons ($\gamma$). The measured fiducial parton-level cross section is $\sigma_{tq\gamma}\times\mathcal{B}(t\rightarrow l\nu b) = 688\pm 23(\text{stat.})^{+75}_{-71}(\text{syst.})\,$fb.

This table lists the kinematic requirements on particle-level objects used to define of the fiducial phase space for the particle-level measurement. The particle level objects are photons ($\gamma$) not from a hadron decay, neutrinos not from a hadron decay ($\nu$), prompt electrons and muons ($\ell$) "dressed" by adding close-by ($\Delta R < 0.1$) photons, and anti-$k_t$ $R = 0.4$ jets built from stable particles ($\tau > 30\,$ps) and tau leptons excluding neutrinos and prompt dressed muons. Jets are $b$-tagged ($b$-jet) using ghost-matched $b$-hadrons with $p_{\text{T}} > 5\,$GeV. Apart from the kinematic requirements, isolation and overlap removal criteria are applied. Jets within $\Delta R = 0.4$ of a photon are removed if the $p_{\text{T}}$ of charged particles within $\Delta R = 0.3$ of the photon is smaller than $10\,\%$ of its $p_{\text{T}}$. Jets within $\Delta R = 0.4$ of a lepton are removed. Events are removed where a photon is close ($\Delta R < 0.4$) to a lepton or a surviving jet. The measured fiducial particle-level cross section is $\sigma_{tq\gamma}\times\mathcal{B}(t\rightarrow l\nu b)+\sigma_{t(\rightarrow l\nu b\gamma)q} = 303\pm 9(\text{stat.})^{+33}_{-32}(\text{syst.})\,$fb.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Summary of the total uncertainty in the background prediction for each SR of the tt0L-low, tt0L-high, tt1L and tt2L analysis channels in the statistical combination. Their dominant contributions are indicated by individual lines. Individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

$E_{\text{T}}^{\text{miss}}$ distribution in SR0X for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

$E_{\text{T}}^{\text{miss}}$ distribution in SRWX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

$E_{\text{T}}^{\text{miss}}$ distribution in SRTX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Representative fit distribution in the different flavour leptons signal region for the tt2L analysis: each bin of such distribution, starting from the red arrow, corresponds to a single SR included in the fit. 'FNP' includes the contribution from fake/non-prompt lepton background arising from jets (mainly $\pi/K$, heavy-flavour hadron decays and photon conversion) misidentified as leptons, estimated in a purely data-driven way. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of selected reconstructed events divided by the acceptance.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of selected reconstructed events divided by the acceptance.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of selected reconstructed events divided by the acceptance.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Parametrization of the $A_{X}$ factor, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level, as function of $m_{X}$ obtained from the narrow width simulated signal samples produced in gluon fusion.

The correction factor, $C_{X}$, defined as the ratio between the number of reconstructed signal events passing the analysis cuts and the number of signal events at the particle level generated within the fiducial volume, and acceptance correction factor, $A_{X}$, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level. Both are computed for NWA spin-0 models as a function of $m_{X}$.

Effect of event selections on a scalar MC signal sample generated for $m_{X}$ = 15 GeV and on the data. For the MC sample, the efficiencies are shown after applying event weights and a truth level filter that requires two photons with $p^{\gamma\gamma}_{T}>40$ GeV; for the data, the absolute yields are shown. The initial yields for data include a trigger preselection that is the OR of a list of single photon and diphoton triggers. The "2 $loose$ photons" step includes the kinematic acceptance cuts.

Parameterization of the Double Sided Crystal Ball function parameters describing the scalar mass resolution model as a function of $m_{X}$ [GeV].

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a W boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a Z boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Observed 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Observed 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Data and post-fit signal and background from S+B fit for 315 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 550 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for a 790 GeV narrow-width pseudoscalar resonance decaying to a Z boson and a 300 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 420 GeV large-width pseudoscalar resonance decaying to a Z boson and a 320 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 700 GeV large-width pseudoscalar resonance decaying to a Z boson and a 380 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for SM VHH production, with each Higgs boson decaying to $2b$.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the hadhad category. The efficiencies are calculated with respect to HH to bbtautau decays in which both tau-leptons decay hadronically. The ‘Pre-selection’ consists of basic requirements, including that at least two tau-had-vis pass loose kinematic requirements, at least one of the tau-had-vis candidate passes identification requirement, and that events do not contain an electron or muon. The ‘Object selections’ require exactly two tau-had-vis candidates, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Loose tau ID’ requires that both tau-had-vis candidates pass identification requirements. The ‘DTT offline jet cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad SLT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad LTT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Post-fit expected number of signal and background events and observed number of data events after applying the selection criteria and requiring exactly 2 b-tagged jets and assuming a background-only hypothesis.

Observed and expected limits at 95% CL on the cross-section of HH production, for the non-resonant ggF+VBF HH search, and the resonant HH search for four values of the resonance mass mX.

Acceptance times efficiency for the full analysis selections as a function of the resonance mass mX in the hadhad, lephad SLT and lephad LTT trigger categories, and the combined lephad. The acceptance times efficiency is evaluated for HH to bbtautau decays, with respect to the targeted tau-lepton decay modes (lephad or hadhad).

Post-fit distribution of mHH in the hadhad channel.

Post-fit distribution of mHH in the lephad SLT channel.

Post-fit distribution of mHH in the lephad LTT channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of of di-b-jet mass in the hadhad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

BDT for SM HH in the hadhad channel

NN for SM HH in the lephad SLT channel

NN for SM HH in the lephad LTT channel

PNN for mX = 500 GeV resonant HH in the hadhad channel

PNN for mX = 500 GeV resonant HH in the lephad SLT channel

PNN for mX = 500 GeV resonant HH in the lephad LTT channel

PNN for mX = 1000 GeV resonant HH in the hadhad channel

PNN for mX = 1000 GeV resonant HH in the lephad SLT channel

PNN for mX = 1000 GeV resonant HH in the lephad LTT channel

Event yields as a function of log10(S/B) for data, background and non-resonant HH signal. Final discriminant bins from the hadhad, lephad SLT and lephad LTT categories are combined into bins of log10(S/B). The B is the fitted background yield assuming background-only hypothesis, and the signal S is scaled to the SM expected cross-section.

Observed and expected limits at 95% CL on the cross-section of the resonant HH production as a function of the scalar resonance mass mX.

Post-fit distribution of delta-R between the taus in the hadhad channel.

Post-fit distribution of delta-R between the b-tagged jets in the hadhad channel.

Post-fit distribution of delta-R between the taus in the lephad SLT channel.

Post-fit distribution of delta-R between the b-tagged jets in the lephad SLT channel.

Post-fit distribution of delta-pT between the tau and lepton in the lephad SLT channel.

Post-fit distribution of pT of the subleading b-tagged jet in the lephad SLT channel.

Post-fit distribution of MTW in the lephad SLT channel.

Post-fit distribution of missing transverse momentum in the lephad SLT channel.

Post-fit distribution of missing transverse momentum centrality in the lephad SLT channel.

Post-fit distribution of delta-phi between the Higgs boson candidates in the lephad SLT channel.

Post-fit distribution of delta-pT between the tau and lepton in the lephad LTT channel.

Post-fit distribution of delta-R between the taus in the lephad LTT channel.

Post-fit distribution of delta-phi between the lepton and the missing transverse momentum in the lephad LTT channel.

Post-fit distribution of delta-phi between the Higgs boson candidates in the lephad LTT channel.

Post-fit distribution of the total transverse momentum s in the lephad LTT channel.

PNN for mX = 300 GeV resonant HH in the hadhad channel

PNN for mX = 300 GeV resonant HH in the lephad SLT channel

PNN for mX = 300 GeV resonant HH in the lephad LTT channel

PNN for mX = 1600 GeV resonant HH in the hadhad channel

PNN for mX = 1600 GeV resonant HH in the lephad SLT channel

PNN for mX = 1600 GeV resonant HH in the lephad LTT channel

Local p-value of the background-only hypothesis as a function of the resonance mass.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the signal region, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $W$+jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Expected distributions of the MEM discriminant $P(S|X)$ in the SR, for the s-channel single-top signal, and for the $t\bar{t}$ and $W$+jets backgrounds, for MEM discriminant values larger than $2.0\times10^{-4}$. Each distribution is normalised to unity. The binning is the same as the optimised binning used in the signal extraction fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $W$+jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 3-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 4-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty bands indicate the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR before the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore not included. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$, after subtraction of all backgrounds. The fitted distribution for the simulation of the signal is shown together with the post-fit uncertainty in the backgrounds. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Pre-fit and post-fit event yields in the SR, for MEM discriminant values larger than $2.0\times10^{-4}$. The central value of the event yield for each process is calculated by summing the values of the discriminant bin contents, using the nominal expected yield for the pre-fit value, and the best-fit estimate for the post-fit value. The error includes statistical and systematic uncertainties summed in quadrature. All sources of systematic uncertainties are included, taking into account correlations and anti-correlations in the post-fit case. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore only included in the post-fit uncertainties.

Observed impact of the different sources of uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. The 'Systematic uncertainties' category combines all sources of systematic uncertainties. The statistical uncertainty is obtained by repeating the fit after having fixed all nuisance parameters, including the $t\bar{t}$ and $W$+jets normalisation factors. 'Total' gives the total uncertainty on the measurement.

Observed impact of the different sources of $t\bar{t}$ modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and 'ME/PS matching' to the matching of the ME to the parton shower.

Observed impact of the different sources of s-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of t-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of $tW$ modelling uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and '$t\bar{t}$ overlap' to the algorithm removing the overlap between $tW$ and $t\bar{t}$ production at NLO, as described in Section 7 in the paper.

Observed impact of the different sources of PDF uncertainties on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the ${E}_{T}^{miss}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the $m_{T}^{W}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Nuisance parameters ranked according to their post-fit impacts on the best-fit value of the ratio $\mu$ of the measured cross-section to the predicted cross-section. In the figure, only the 20 nuisance parameters with the largest post-fit impacts are shown. The empty (solid) blue rectangles illustrate the pre-fit (post-fit) impact on $\mu$, corresponding to the upper axis. The pre-fit (post-fit) impact of each nuisance parameter, $\Delta\mu$, is calculated as the difference in the fitted value of $\mu$ between the nominal fit and the fit when fixing the corresponding nuisance parameter to $\hat{\theta}\pm\Delta\theta$ ($\hat{\theta}\pm\Delta\hat{\theta}$), where $\hat{\theta}$ is the best-fit value of the nuisance parameter and $\Delta\theta$ ($\Delta\hat{\theta}$) is its pre-fit (post-fit) uncertainty. Several systematic uncertainties are split into different nuisance parameters, which are indicated by NP. JES (JER) indicates jet energy scale (resolution), and $\gamma$ indicates a nuisance parameter associated to the MC statistics in one of the 18 bins numbered from 0 to 17. The black points show the best-fit values of the nuisance parameters, with the error bars representing the post-fit uncertainties. Each nuisance parameter is shown wrt. its nominal value, $\theta_0$, and in units of its pre-fit uncertainty, except the free-floating normalisation factors of the $t\bar{t}$ and $W$+jets backgrounds, and the parameters associated to the MC statistics in each bin, for which the post-fit values and uncertainties are shown.

Correlation matrix of the nuisance parameters and of the ratio $\mu$ of the measured cross-section to the predicted cross-section. The correlations are given after the fit to data. In the figure, only the parameters which have a correlation of at least 0.2 with any other parameter are shown.

Distribution of the MEM discriminant $P(S|X)$ in the SR for MEM discriminant values larger than $2.0\times10^{-4}$, for the collision data used for the measurement, and for 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Measured values of the signal cross-section and of the $t\bar{t}$ and $W$+jets normalisation factors, obtained by statistical-only fits to the collision data used for the measurement, and to 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The central values and their statistical uncertainties are obtained by repeating the fit after having fixed all nuisance parameters, except the $t\bar{t}$ and $W$+jets normalisation factors, which are let free-floating (unlike for the statistical uncertainty on the cross-section quoted in the paper). The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$.

Signal region detector-level distribution for the observable $m_{e\mu}$.

Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{e\mu}$.

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

Breakdown of expected and observed yields in the electroweak search Int, Low, and OffShell signal regions after a simultaneous fit to the signal regions and control regions. All statistical and systematic uncertainties are included.

Breakdown of expected and observed yields in the four edge signal regions, integrated over the $m_{\ell\ell}$ distribution after a separate simultaneous fit to each signal region and control region pair. The uncertainties include both the statistical and systematic sources.

Breakdown of expected and observed yields in the three on-$Z$ signal regions after a separate simultaneous fit to each signal region and control region pair. The uncertainties include both the statistical and systematic sources.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.

Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.

Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.

Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.

Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.

Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.

Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.

Observed and expected dilepton mass distributions in VR3L-STR without a fit to the data. The 'Other' category includes the negligible contributions from $t\bar{t}$ and $Z/\gamma^*$+jets processes. The hatched band contains the statistical uncertainty and the theoretical systematic uncertainties of the $WZ$/$ZZ$ prediction, which are the dominant sources of uncertainty. No fit is performed. The last bin contains the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.

Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.

Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.

Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294]. The grey numbers indicate the observed 95\% CLs upper limit on the cross section.

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294]. The grey numbers indicate the observed 95$\%$ CLs upper limit on the cross section.

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

The combined $E_{\mathrm{T}}^{\mathrm{miss}}$ distribution of VRLow-STR and SRLow-STR (left), and the same region with the $\Delta\phi(\boldsymbol{j}_{1,2},\boldsymbol{\mathit{p}}_{ ext{T}}^{ ext{miss}})<0.4$ requirement, used as a control region to normalize the $Z/\gamma^*+\mathrm{jets}$ process (right). Separate fits for the SR and VR are performed, as for the results in the paper, and the resulting distributions are merged. All statistical and systematic uncertainties are included in the hatched bands. The last bins contain the overflow.

Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.

Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.

Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.

Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.

Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.

Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.

Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.

Table 36: Cutflow of expected events in the region SR-OffShell_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 36: Cutflow of expected events in the region SR-OffShell_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 37: Cutflow of expected events in the region SR-OffShell_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 37: Cutflow of expected events in the region SR-OffShell_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 38: Cutflow of expected events in the region SR-Low_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 38: Cutflow of expected events in the region SR-Low_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 39: Cutflow of expected events in the region SR-Low_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 39: Cutflow of expected events in the region SR-Low_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 40: Cutflow of expected events in the region SR-Low-2-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 40: Cutflow of expected events in the region SR-Low-2-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 41: Cutflow of expected events in the region SR-Int_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 41: Cutflow of expected events in the region SR-Int_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 42: Cutflow of expected events in the region SR-Int_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 42: Cutflow of expected events in the region SR-Int_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 43: Cutflow of expected events in the region SR-High_16a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 43: Cutflow of expected events in the region SR-High_16a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 44: Cutflow of expected events in the region SR-High_16b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 44: Cutflow of expected events in the region SR-High_16b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 45: Cutflow of expected events in the region SR-High_8a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 45: Cutflow of expected events in the region SR-High_8a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 46: Cutflow of expected events in the region SR-High_8b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 46: Cutflow of expected events in the region SR-High_8b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 47: Cutflow of expected events in the region SR-1J-High-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 47: Cutflow of expected events in the region SR-1J-High-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 48: Cutflow of expected events in the region SR-llbb-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 48: Cutflow of expected events in the region SR-llbb-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

The combined $m_{jj}$ distribution of CR-Z-EWK and SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in CR-Z-met-EWK (right), which removes the upper limit of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}}) < 9$ from the definition of CR-Z-EWK. This $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ tail overlaps other control and validation regions, but not signal regions. The arrows indicate the signal region SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ phase space which is not included in CR-Z-EWK (right). All EWK search control and signal regions are included in the fit. All statistical and systematic uncertainties are included in the hatched bands. The theoretical uncertainties from CR-Z-EWK are applied to CR-Z-met-EWK. The last bins contain the overflow.

The combined $m_{jj}$ distribution of CR-Z-EWK and SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in CR-Z-met-EWK (right), which removes the upper limit of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}}) < 9$ from the definition of CR-Z-EWK. This $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ tail overlaps other control and validation regions, but not signal regions. The arrows indicate the signal region SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ phase space which is not included in CR-Z-EWK (right). All EWK search control and signal regions are included in the fit. All statistical and systematic uncertainties are included in the hatched bands. The theoretical uncertainties from CR-Z-EWK are applied to CR-Z-met-EWK. The last bins contain the overflow.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.