The definitions of the four tetralepton signal regions. The regions are defined to target different $b$-jet multiplicities and flavour combinations of the non-Z leptons.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|$ in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel. The uncertainty is decomposed into four components which are the signal modelling uncertainty, the background modelling uncertainty, the experimental uncertainty, and the data statistical uncertainty.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised parton-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the absolute particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}$ of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the absolute value of rapidity of the $Z$ boson in the 3$\ell$+4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{l \textrm{non-}Z}$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 3$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $|\Delta \phi (t\bar{t}, Z)|/\pi$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the $p_{\textrm{T}}^{t\bar{t}}$ in the 4$\ell$ channel.

The total correlation matrix of the normalised particle-level differential cross-section measured in the fiducial phase-space as a function of the number of jets in the 4$\ell$ channel.

The $m_{\mathrm{eff}}$ distribution in SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.

The $m_{\mathrm{eff}}$ distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.

The $m_{\mathrm{eff}}$ distribution in SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$ and SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.

The $m_{\mathrm{eff}}$ distribution in SR0$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.

The $m_{\mathrm{eff}}$ distribution in SR1$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.

The $m_{\mathrm{eff}}$ distribution in SR2$_{\mathrm{breq}}$ for events passing the signal region requirements except the $m_{\mathrm{eff}}$ requirement. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band. The red arrows indicate the $m_{\mathrm{eff}}$ selections in the signal regions.

Expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on the higgsino GGM models. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ bserved 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on wino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on slepton/sneutrino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{12k}$, where $k \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ expected 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$+1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

$-1\sigma$ observed 95% CL exclusion limits on gluino NLSP pair production with RPV LSP decays via $\lambda_{i33}$, where $i \in{1,2}$. The limits are set using the statistical combination of disjoint signal regions. Where two (or more) signal regions overlap, the signal region contributing its observed $\mathrm{CL}_{\mathrm{s}}$ value to the combination is the one with the better (best) expected $\mathrm{CL}_{\mathrm{s}}$ value.

Observed upper limit on the signal cross section in fb for the wino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the wino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the gluino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the gluino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Observed upper limit on the signal cross section in fb for the higgsino GGM models. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Best expected SR for the wino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the wino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the slepton/sneutrino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the gluino NLSP models with RPV LSP decays via $\lambda_{12k}$ where $k \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the gluino NLSP models with RPV LSP decays via $\lambda_{i33}$ where $i \in{1,2}$. A value of 1 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 2 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, 3 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, 4 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and 5 corresponds to SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$.

Best expected SR for the higgsino GGM models. A value of 6 corresponds to SR0-ZZ$^{\mathrm{loose}}$, 7 corresponds to SR0-ZZ$^{\mathrm{tight}}$, 8 corresponds to SR0-ZZ$^{\mathrm{loose}}_{\mathrm{bveto}}$, and 9 corresponds to SR0-ZZ$^{\mathrm{tight}}_{\mathrm{bveto}}$.

Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{12k}\neq 0$ models for SR0$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR1$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the wino NLSP $\lambda_{i33}\neq 0$ models for SR2$_{\mathrm{breq}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the GGM Higgsino grid for SR0-ZZ$^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Acceptance across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

Efficiency across the GGM Higgsino grid for SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$. The interpolation between signal scenarios studied is included for illustration purposes only and may be subject to interpolation effects in sparsely populated areas.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$^{\mathrm{tight}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR5L. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light leptons in distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the taus leptons in distribution in SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The $p_{\mathrm{T}}$ of the light taus in distribution in SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with four light leptons and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with four light leptons and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with four light leptons and two Z candidates. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with exactly five light leptons. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with three light leptons and one tau and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with three light leptons and one tau and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with two light leptons and two taus and a Z veto. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

The lepton flavour and multiplicities in events with two light leptons and two taus and one Z candidate. Distributions for data, the estimated SM backgrounds after the background-only fit, and an example SUSY scenario are shown. "Other" is the sum of the $tWZ$, $t\bar{t}WW$, $t\bar{t} ZZ$, $t\bar{t} WH$, $t\bar{t} HH$, $t\bar{t} tW$, and $t\bar{t}t\bar{t}$ backgrounds. The last bin captures the overflow events. The lower panel shows the ratio of the observed data to the expected SM background yield in each bin. Both the statistical and systematic uncertainties in the SM background are included in the shaded band.

Cutflow event yields in regions SR0$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR0$_{\mathrm{bveto}}^{\mathrm{tight}}$, SR0$_{\mathrm{breq}}$, and SR5L for RPV models with the $\lambda_{12k}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR1$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR1$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR1$_{\mathrm{breq}}$ for RPV models with the $\lambda_{i33}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR2$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR2$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR2$_{\mathrm{breq}}$ for RPV models with the $\lambda_{i33}\neq 0$ coupling. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} >$ 9 GeV are required.

Cutflow event yields in regions SR0-ZZ$^{\mathrm{loose}}$, SR0-ZZ$^{\mathrm{tight}}$, SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{loose}}$, SR0-ZZ$_{\mathrm{bveto}}^{\mathrm{tight}}$, and SR5L the higgsino GGM RPC model with BR($\tilde{\chi}^{0}_1 \rightarrow Z \tilde{G}$) = 50% and higgsino masses of 200 GeV, or BR($\tilde{\chi}^{0}_1 \rightarrow Z \tilde{G}$) = 100% and higgsino masses of 300 GeV. All yields correspond to weighted events, so that effects from lepton reconstruction efficiencies, trigger corrections, pileup reweighting, etc., are included. They are normalized to the integrated luminosity of the data sample, $\int L dt = 139\,\mbox{fb\(^{-1}\)}$. The generator filter is a selection of $\geq$4e/$\mu$/$\tau_{\mathrm{had-vis}}$ leptons with $p_{\mathrm{T}}(e,\mu)>4$GeV, $p_{\mathrm{T}}(\tau_{\mathrm{had-vis}})>15$GeV and $|\eta|<2.8$ and is applied during the MC generation of the simulated events. The preliminary event reduction is a centralized stage where at least two electrons/muons with uncalibrated $p_{\mathrm{T}} > 9$ GeV are required.

Efficiency in the Single-bin SR as a function of the M(Sbottom) vs. M(N2) with the $\Delta$ M(N2,N1) $= 130$ GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.

Acceptance in the Multi-bin SR, $\min_{\Theta} < 0.5$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.

Efficiency in the Multi-bin SR, $\min_{\Theta} < 0.5$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.

Acceptance in the Multi-bin SR, $0.5 < \min_{\Theta} < 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.

Efficiency in the Multi-bin SR, $0.5 < \min_{\Theta} < 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.

Acceptance in the Multi-bin SR, $\min_{\Theta} > 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the acceptance is given in units of $10^{-4}$.

Efficiency in the Multi-bin SR, $\min_{\Theta} > 1.0$ bin as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV. Keep in mind that the efficiency is given in units of $10^{-2}$.

Observed upper limits on the signal cross section as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV.

Expected upper limits on the signal cross section as a function of the M(Sbottom) vs. M(N2) with the $\Delta M$(N2,N1) = 130 GeV.

Cutflows for the bechmarl signal point M(Sbottom) = 800 GeV, M(N2) = 180 GeV. Weighted event yields are reported starting with the "Preselection" line, normalized to an integrated luminosity of $139$ fb$^{−1}$.

Comparison of the expected and observed event yields in the signal regions. The top-quark and Z(mumu) background contributions are scaled with the normalization factors obtained from the background-only fit. The other contribution includes all the backgrounds not explicitly listed in the legend (V+jets except Z(mumu)+jets, di-/triboson, multijet). The hatched band indicates the total statistical and systematic uncertainties in the SM background. The contributions from three signal models to the signal regions are also displayed, where the masses M(Sbottom) and M(N2) are given in GeV in the legend. The lower panel shows the significance of the deviation of the observed yield from the expected background yield.

Dominant systematic uncertainties in the background prediction for the signal regions after the fit to the control regions. “Other” includes the uncertainties arising from muons, jet-vertex tagging, modeling of pile-up, the $E_{T}^{miss}$ computation, multijet background, and luminosity. The individual uncertainties can be correlated and do not necessarily add up quadratically to the total uncertainty.

Inclusive differential cross section for two electrons and two muons (Max = 1710~GeV).

Mass of the first dilepton pair in the $Z$ mass region (Max = 88.1~GeV).

Mass of the first dilepton pair in the $H$ mass region (Max = 107~GeV).

Mass of the first dilepton pair in the offshell region (Max = 146~GeV).

Mass of the first dilepton pair in the $ZZ$ mass region (Max = 232~GeV).

Mass of the second dilepton pair in the $Z$ mass region (Max = 44.2~GeV).

Mass of the second dilepton pair in the $H$ mass region (Max = 59.6~GeV).

Mass of the second dilepton pair in the offhell region (Max = 88.3~GeV).

Mass of the second dilepton pair in the $ZZ$ mass region (Max = 1000~GeV).

$p_T$ of the first dilepton pair in the $Z$ mass region (Max = 291~GeV).

$p_T$ of the first dilepton pair in the $H$ mass region (Max = 245~GeV).

$p_T$ of the first dilepton pair in the offshell region (Max = 216~GeV).

$p_T$ of the first dilepton pair in the $ZZ$ mass region (Max = 555~GeV).

$p_T$ of the second dilepton pair in the $Z$ mass region (Max = 117~GeV).

$p_T$ of the second dilepton pair in the $H$ mass region (Max = 124~GeV).

$p_T$ of the second dilepton pair in the offhell region (Max = 192~GeV).

$p_T$ of the second dilepton pair in the $ZZ$ mass region (Max = 555~GeV).

$\cos\theta^*$ between first dilepton pair in the $Z$ mass region.

$\cos\theta^*$ between first dilepton pair in the offshell region.

$\cos\theta^*$ between first dilepton pair in the $H$ mass region.

$\cos\theta^*$ between first dilepton pair in the $ZZ$ mass region.

$\cos\theta^*$ between second dilepton pair in the $Z$ mass region.

$\cos\theta^*$ between second dilepton pair in the $H$ mass region.

$\cos\theta^*$ between second dilepton pair in the offshell mass region.

$\cos\theta^*$ between second dilepton pair in the $ZZ$ mass region.

$\Delta y$ between the dilepton pairs in the $Z$ mass region.

$\Delta y$ between the dilepton pairs in the $H$ mass region.

$\Delta y$ between the dilepton pairs in the offshell region.

$\Delta y$ between the dilepton pairs in the $ZZ$ mass region.

$\Delta \phi$ between the dilepton pairs in the $Z$ mass region.

$\Delta \phi$ between the dilepton pairs in the $H$ mass region.

$\Delta \phi$ between the dilepton pairs in the offshell region.

$\Delta \phi$ between the dilepton pairs in the $ZZ$ mass region.

$\Delta \phi$ between the leading lepton pairs in the $Z$ mass region.

$\Delta \phi$ between the leading lepton pairs in the $H$ mass region.

$\Delta \phi$ between the leding lepton pairs in the offshell region.

$\Delta \phi$ between the leading lepton pairs in the $ZZ$ mass region.

Four lepton mass in first $p_T^{4l}$ slice (Max = 726~GeV)

Four lepton mass in second $p_T^{4l}$ slice (Max = 1079~GeV)

Four lepton mass in third $p_T^{4l}$ slice (Max = 1153~GeV)

Four lepton mass in fourth $p_T^{4l}$ slice (Max = 1320~GeV)

Four lepton mass in fifth $p_T^{4l}$ slice (Max = 1413~GeV)

Four lepton mass in first $y^{4l}$ slice (Max = 1710~GeV)

Four lepton mass in second $y^{4l}$ slice (Max = 1116~GeV)

Four lepton mass in third $y^{4l}$ slice (Max = 1413~GeV)

Four lepton mass in fourth $y^{4l}$ slice (Max = 1044~GeV)

Four lepton mass in fifth $y^{4l}$ slice (Max = 918~GeV)

Covariance matrix for M4l

Covariance matrix for M4lvChannel

Covariance matrix for m12_vs_M4l

Covariance matrix for m34_vs_M4l

Covariance matrix for pt12_vs_M4l

Covariance matrix for pt34_vs_M4l

Covariance matrix for CTS12_vs_M4l

Covariance matrix for CTS34_vs_M4l

Covariance matrix for dYpairs_vs_M4l

Covariance matrix for dPhiPairs_vs_M4l

Covariance matrix for dPhiLeadLep_vs_M4l

Covariance matrix for M4lvPt4lbin

Covariance matrix for M4lvRapiditybin

EFT results (linear)

EFT results (full)

Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Three-body selection. Distributions of $M_{\Delta}^R$ in (a,b) $SR_{W}^{3-body}$ and (c,d) $SR_{T}^{3-body}$ for (left) same-flavour and (right) different-flavour events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panels indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Four-body selection. (a) distributions of $E_{T}^{miss}$ in $SR^{4-body}_{Small\,\Delta m}$ and (b) distribution of $R_{2\ell 4j}$ in $SR^{4-body}_{Large\,\Delta m}$ for events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panel indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Four-body selection. (a) distributions of $E_{T}^{miss}$ in $SR^{4-body}_{Small\,\Delta m}$ and (b) distribution of $R_{2\ell 4j}$ in $SR^{4-body}_{Large\,\Delta m}$ for events satisfying the selection criteria of the given SR, except the one for the presented variable, after the background fit. The contributions from all SM backgrounds are shown as a histogram stack. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. The hatched bands represent the total statistical and systematic uncertainty. The rightmost bin of each plot includes overflow events. Reference top squark pair production signal models are overlayed for comparison. Red arrows in the upper panel indicate the signal region selection criteria. The bottom panels show the ratio of the observed data to the total SM background prediction, with hatched bands representing the total uncertainty in the background prediction; red arrows show data outside the vertical-axis range.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the Observed limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}_1^0$ with 100\% branching ratio, in the (a) $m(\tilde{t}_1)$--$m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{t}_1)$--$\Delta m(\tilde{t}_1,\tilde{\chi}_1^0)$ planes. The dashed lines and the shaded bands are the expected limits and their $\pm1\sigma$ uncertainties. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. 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 shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. 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 shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. 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 shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the mediator mass for a DM particle mass of $m(\chi)=1$ GeV. 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 shows the observed (expected) exclusion limits.

Two-body selection. Background fit results for $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}}$, $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}Z}$, $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t}, DF}$, $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t}, SF}$ and $\mathrm{VR}^{\mathrm{2-body}}_{t\bar{t} Z}$. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Three-body selection. Background fit results for $\mathrm{CR}^{\mathrm{3-body}}_{t\bar{t}}$, $\mathrm{CR}^{\mathrm{3-body}}_{VV}$, $\mathrm{CR}^{\mathrm{2-body}}_{t\bar{t}Z}$, $\mathrm{VR}^{\mathrm{3-body}}_{VV}$, $\mathrm{VR(1)}^{\mathrm{3-body}}_{t\bar{t}}$ and $\mathrm{VR(2)}^{\mathrm{3-body}}_{t\bar{t}}$. ''Others'' includes contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$ processes. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Four-body selection. Background fit results for $\mathrm{CR}^{\mathrm{4-body}}_{t\bar{t}}$,$\mathrm{CR}^{\mathrm{4-body}}_{VV}$, $\mathrm{VR}^{\mathrm{4-body}}_{t\bar{t}}$, $VR^{4-body}_{VV}$ and $\mathrm{VR}^{\mathrm{4-body}}_{VV,lll}$. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Two-body selection. Background fit results for the different-flavour leptons binned SRs. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Two-body selection. Background fit results for the same-flavour leptons binned SRs. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Three-body selection. Observed event yields and background fit results for the three-body selection SRs. The ''Others'' category contains contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Entries marked `--' indicate a negligible background contribution (less than 0.001 events). The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Four-body selection. Observed event yields and background fit results for SR$^{\mathrm{4-body}}_{\mathrm{Small}\,\Delta m}$ and SR$^{\mathrm{4-body}}_{\mathrm{Large}\,\Delta m}$. The ''Others'' category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. The individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Exclusion limits contours (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with 100% branching ratio in $\tilde{t}_1--\tilde{\chi}^0_1$ masses planes. The dashed lines and the shaded bands are the expected limit and its $\pm 1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The exclusion limits contours for the two-body, three-body and four-body selections are respectively shown in blue, green and red.

Exclusion limits contours (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with 100% branching ratio in $\tilde{t}_1--\tilde{\chi}^0_1$ masses planes. The dashed lines and the shaded bands are the expected limit and its $\pm 1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The exclusion limits contours for the two-body, three-body and four-body selections are respectively shown in blue, green and red.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow t \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm 1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b W \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty. The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty.The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limit contour (95% CL) for a simplified model assuming $\tilde{t}_1$ pair production, decaying via $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}_1^0$ with 100% branching ratio, in $\tilde{t}_1$--$\tilde{\chi}_1^0$ masses plane. The dashed lines and the shaded bands are the expected limit and its $\pm1\sigma$ uncertainty.The thick solid lines are the observed limits for the central value of the signal cross-section. The expected and observed limits do not include the effect of the theoretical uncertainties in the signal cross-section. The dotted lines show the effect on the observed limit when varying the signal cross-section by $\pm1\sigma$ of the theoretical uncertainty. The observed (a) and expected (b) CLs values are respectively shown.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. 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 shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. 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 shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. 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 shows the observed (expected) exclusion limits.

Exclusion limits for (a) $t\bar{t} + \phi $ scalar and (b) $t\bar{t} + a $ pseudoscalar models as a function of the DM particle mass for a mediator mass of 10 GeV. 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 shows the observed (expected) exclusion limits.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection efficiency (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection efficiency (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Four-body selection Efficiency (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.

Four-body selection Efficiency (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta\ m}$ for a simplified model assuming $\tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $ t \tilde{t} +\phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + \phi$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-DF$^{2-body}_{[110,120)}$, (b) SR-DF1$^{2-body}_{[120,140)}$, (c) SR-DF2$^{2-body}_{[140,160)}$, (d) SR-DF3$^{2-body}_{[160,180)}$, (e) SR-DF4$^{2-body}_{[180,220)}$, (f) SR-DF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) SR-SF$^{2-body}_{[110,120)}$, (b) SR-SF1$^{2-body}_{[120,140)}$, (c) SR-SF2$^{2-body}_{[140,160)}$, (d) SR-SF3$^{2-body}_{[160,180)}$, (e) SR-SF4$^{2-body}_{[180,220)}$, (f) SR-SF5$^{2-body}_{[220,\infty)}$ for a simplified model assuming $t \tilde{t} + a$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Two-body selection acceptance (a) $SR^{2-body}_{[110,\infty)}$ , (b) $SR^{2-body}_{[120,\infty)}$ , (c) $SR^{2-body}_{[140,\infty)}$ , (d) $SR^{2-body}_{[160,\infty)}$ , (e) $SR^{2-body}_{[180,\infty)}$ , (f) $SR^{2-body}_{[200,\infty)}$ , (g) $SR^{2-body}_{[220,\infty)}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Three-body selection acceptance (a) SR-DF$^{3-body}_{t}$, (b) SR-SF$^{3-body}_{t}$, (c) SR-DF$^{3-body}_{W}$, (d) SR-SF$^{3-body}_{W}$ for a simplified model assuming $ \tilde{t}_1$ pair production.

Four-body selection acceptance (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.

Four-body selection acceptance (a) SR$^{4-body}_{Small \Delta m}$ , (b) $SR^{4-body}_{Large \Delta m}$ for a simplified model assuming $\tilde{t}_1$ pair production.

Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Two-body selection The numbers indicate the observed upper limits on the signal strenght for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Three-body selection The numbers indicate the upper limits on the signal strenght for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.

Four-body selection The numbers indicate the upper limits on the signal strenght for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.

Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Two-body selection The numbers indicate the upper limits on the signal cross-section for (a) a simplified model assuming $\tilde{t}_1$ pair production, (b) for $t\tilde{t} + a $ pseudoscalar models, (c) for $t\tilde{t} + \phi $ scalar models. In Figure (a), the red line corresponds to the observed limit.

Three-body selection The numbers indicate the upper limits on the signal cross-section for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.

Four-body selection The numbers indicate the upper limits on the signal cross-section for a simplified model assuming $\tilde{t}_1$ pair production. For comparison, the red line corresponds to the observed limit.

Two-body selection. Background fit results for the $inclusive$ SRs. The Others category contains the contributions from $VVV$, $t\bar{t} t$, $t\bar{t}t\bar{t}$, $t\bar{t} W$, $t\bar{t} WW$, $t\bar{t} WZ$, $t\bar{t} H$, and $tZ$. Combined statistical and systematic uncertainties are given. Note that the individual uncertainties can be correlated, and do not necessarily add up quadratically to the total background uncertainty.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow t^{(*)}\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=600~ GeV$ and $m(\tilde{\chi}^0_1)=400~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the scalar signal model $t\bar{t} + \phi $ with $m(\phi)=150~ GeV$ and $m(\chi)=1~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the pseudoscalar signal model $t\bar{t} + a $ with $m(a)=150~ GeV$ and $m(\chi)=1~ GeV$ in the SRs for the two-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=385~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=400~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=430~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow bW\tilde{\chi}^0_1$ with $m(\tilde{t}_1)=550~ GeV$ and $m(\tilde{\chi}^0_1)=460~ GeV$ in the SRs for the three-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=400~ GeV$ and $m(\tilde{\chi}^0_1)=380~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=460~ GeV$ and $m(\tilde{\chi}^0_1)=415~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Cut flow for the simplified signal model $\tilde{t}_1 \rightarrow b l \nu \tilde{\chi}^0_1$ with $m(\tilde{t}_1)=400~ GeV$ and $m(\tilde{\chi}^0_1)=320~ GeV$ in the SRs for the four-body selection. The number of events is normalized to the cross-section and to an integrated luminosity of $139~fb^{-1}$.

Distribution of $S$, which is one of the discriminating variables used to define the $2\ell^{sc}$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $E_{T}^{miss}$, which is one of the discriminating variables used to define the $3\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $\Delta R_{\ell^{\pm}\ell^{\pm}}$, which is one of the discriminating variables used to define the $3\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $m_{x\ell}$ ($x$=3), which is one of the discriminating variables used to define the $3\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $p_{T}^{leading jet}$, which is one of the discriminating variables used to define the $3\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $E_{T}^{miss}$, which is one of the discriminating variables used to define the $4\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $\Delta R_{\ell^{\pm}\ell^{\pm}}^{min}$, which is one of the discriminating variables used to define the $4\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $m_{x\ell}$ ($x$=4), which is one of the discriminating variables used to define the $4\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Distribution of $p_{T}^{\ell_{1}}$, which is one of the discriminating variables used to define the $4\ell$ SRs. The events are selected with the preselection requirements listed in Table 4 in the paper. The data (dots) are compared with the expected contributions from the relevant background sources (histograms). The expected signal distributions for $m_{H^{\pm\pm}} = 300~GeV$ are also shown, scaled to the observed number of events. The last bin includes overflows.

Contributions from different categories of uncertainties relative to the expected background yields in the defined SRs, as obtained after performing the likelihood ratio test discussed in Section 9 in the paper. The uncertainties are shown for the combination of the individual channels of the $2\ell^{sc}$, $3\ell$ and $4\ell$ SRs. The SRs are indicated along the horizontal axis. In the HEPData entry, the x-axis is simplified for easier visualisation. The first number indicates the sub channel (2:$2\ell^{sc}$, 3:$3\ell$, 4:$4\ell$), while the second number indicates the mass point (2:200, 3:300, 4:400, 5:500).

Data event yields compared with the expected contributions from relevant background sources, for the combination of the individual channels of the $2\ell^{sc}$, $3\ell$ and $4\ell$ SRs. The total uncertainties in the expected event yields are shown as the hatched bands. The SRs are indicated along the horizontal axis. In the HEPData entry, the x-axis is simplified for easier visualisation. The first number indicates the sub channel (2:$2\ell^{sc}$, 3:$3\ell$, 4:$4\ell$), while the second number indicates the mass point (2:200, 3:300, 4:400, 5:500).

The $E_{T}^{miss}$ distribution for the SRs of the $m_{H^{\pm\pm}} = 300~GeV$ signal mass hypothesis, where the selection requirement on $E_{T}^{miss}$ has been removed. In the attached plot, the signals are stacked on top of the backgrounds while individuals contributions of the $2\ell^{sc}$ channel are shown in HEPData. The last bin, isolated by a vertical red dashed line, is inclusive and corresponds to the SR.

The $E_{T}^{miss}$ distribution for the SRs of the $m_{H^{\pm\pm}} = 300~GeV$ signal mass hypothesis, where the selection requirement on $E_{T}^{miss}$ has been removed. In the attached plot, the signals are stacked on top of the backgrounds while individuals contributions of the $3\ell$ channel are shown in HEPData. The last bin, isolated by a vertical red dashed line, is inclusive and corresponds to the SR.

The $E_{T}^{miss}$ distribution for the SRs of the $m_{H^{\pm\pm}} = 300~GeV$ signal mass hypothesis, where the selection requirement on $E_{T}^{miss}$ has been removed. In the attached plot, the signals are stacked on top of the backgrounds while individuals contributions of the $4\ell$ channel are shown in HEPData. The last bin, isolated by a vertical red dashed line, is inclusive and corresponds to the SR.

Observed and expected upper limits of the $H^{\pm\pm}$ pair production cross section times branching fraction at 95% CL obtained from the combination of 2$\ell^{sc}$, 3$\ell$ and 4$\ell$ channels. The region above the observed limit is excluded by the measurement. The bands represent the expected exclusion curves within one and two standard deviations.

The theoretical prediction of Figure 9(a) in the paper.

Observed and expected upper limits of the $H^{\pm\pm}$ and $H^{\pm}$ production cross section times branching fraction at 95% CL obtained from the combination of 2$\ell^{sc}$, 3$\ell$ and 4$\ell$ channels. The region above the observed limit is excluded by the measurement. The bands represent the expected exclusion curves within one and two standard deviations.

The theoretical prediction of Figure 9(b) in the paper.

Data event yields compared with the estimated background in the $m_{H^{\pm\pm}} = 200~GeV$ or $m_{H^{\pm\pm}} = 220~GeV$ SRs. SFOC 0 and SFOC 1,2 refer to the number of same-flavour opposite charge lepton pairs. The total uncertainties in the estimated background yields are shown as the hashed bands. In the HEPData entry, the x-axis is simplified for easier visualisation (1:$e^{\pm}e^{\pm}$, 2:$e^{\pm}\mu^{\pm}$, 3:$\mu^{\pm}\mu^{\pm}$, 4:SFOC 0, 5:SFOC 1,2, 6:$4\ell$).

Data event yields compared with the estimated background in the $m_{H^{\pm\pm}} = 300~GeV$ or $m_{H^{\pm\pm}} = 350~GeV$ SRs. SFOC 0 and SFOC 1,2 refer to the number of same-flavour opposite charge lepton pairs. The total uncertainties in the estimated background yields are shown as the hashed bands. In the HEPData entry, the x-axis is simplified for easier visualisation (1:$e^{\pm}e^{\pm}$, 2:$e^{\pm}\mu^{\pm}$, 3:$\mu^{\pm}\mu^{\pm}$, 4:SFOC 0, 5:SFOC 1,2, 6:$4\ell$).

Data event yields compared with the estimated background in the $m_{H^{\pm\pm}} = 400~GeV$ or $m_{H^{\pm\pm}} = 450~GeV$ SRs. SFOC 0 and SFOC 1,2 refer to the number of same-flavour opposite charge lepton pairs. The total uncertainties in the estimated background yields are shown as the hashed bands. In the HEPData entry, the x-axis is simplified for easier visualisation (1:$e^{\pm}e^{\pm}$, 2:$e^{\pm}\mu^{\pm}$, 3:$\mu^{\pm}\mu^{\pm}$, 4:SFOC 0, 5:SFOC 1,2, 6:$4\ell$).

Data event yields compared with the estimated background in the $m_{H^{\pm\pm}} = 500~GeV$ or $m_{H^{\pm\pm}} = 550~GeV$ or $m_{H^{\pm\pm}} = 600~GeV$ SRs. SFOC 0 and SFOC 1,2 refer to the number of same-flavour opposite charge lepton pairs. The total uncertainties in the estimated background yields are shown as the hashed bands. In the HEPData entry, the x-axis is simplified for easier visualisation (1:$e^{\pm}e^{\pm}$, 2:$e^{\pm}\mu^{\pm}$, 3:$\mu^{\pm}\mu^{\pm}$, 4:SFOC 0, 5:SFOC 1,2, 6:$4\ell$).

Post-fit $m_{T}$ distribution in the SR 4J b-tag N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{T}$ distribution in the SR 6J b-veto N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{T}$ distribution in the SR 6J b-tag N-1 region. N-1 refers to all cuts except for the requirement on $m_T$ being applied. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 2J b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 2J b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 4J low-x b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 4J low-x b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 4J high-x b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 4J high-x b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 6J b-tag signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 6J b-veto signal region. Uncertainties include statistical and systematic uncertainties. Including exemplary signal points. The value 9999 is used as a placeholder for infinity.

Observed 95% CL exclusion contours for the gluino one-step x = 1/2 model.

Expected 95% CL exclusion contours for the gluino one-step x = 1/2 model. space.

Observed 95% CL exclusion contours for the gluino one-step variable-x

Expected 95% CL exclusion contours for the gluino one-step variable-x

Observed 95% CL exclusion contours for the squark one-step x = 1/2 model.

Observed 95% CL exclusion contours for the squark one-step x = 1/2 model.

Observed 95% CL exclusion contours for one-flavour schemes in one-step x = 1/2 model.

Observed 95% CL exclusion contours for one-flavour schemes in one-step x = 1/2 model.

Expected 95% CL exclusion contours for the squark one-step variable-x

Expected 95% CL exclusion contours for the squark one-step variable-x

Expected 95% CL exclusion contours for the squark one-flavour schemes in variable-x

Expected 95% CL exclusion contours for the squark one-flavour schemes in variable-x

Upper limits on the signal cross section for simplified model gluino one-step x = 1/2

Upper limits on the signal cross section for simplified model gluino one-step variable-x

Upper limits on the signal cross section for simplified model squark one-step x = 1/2

Upper limits on the signal cross section for simplified model squark one-step variable-x

Upper limits on the signal cross section for simplified model squark one-step x=1/2 in one-flavour schemes

Upper limits on the signal cross section for simplified model squark one-step variable-x in one-flavour schemes

Post-fit $m_{eff}$ distribution in the 2J b-tag validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 2J b-veto validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 4J b-tag validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 4J b-veto validation region. Uncertainties include statistical and systematic uncertainties. The value 9999 is used as a placeholder for infinity.

Post-fit $m_{eff}$ distribution in the 6J b-tag validation region. Uncertainties include statistical and systematic uncertainties.

Post-fit $m_{eff}$ distribution in the 6J b-veto validation region. Uncertainties include statistical and systematic uncertainties.

Event selection cutflow for two representative signal samples for the SR2JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.

Event selection cutflow for two representative signal samples for the SR2JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.

Event selection cutflow for two representative signal samples for the SR4JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.

Event selection cutflow for two representative signal samples for the SR4JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.

Event selection cutflow for two representative signal samples for the SR6JBT. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.

Event selection cutflow for two representative signal samples for the SR6JBV. The gluino, squark, chargino and neutralino masses are reported. Weighted events including statistical uncertainties are shown.

Signal acceptance in SR2J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR2J discovery high region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR2J discovery low region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx discovery region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx discovery region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Tag bin4 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Veto bin4 region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J discovery high region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR6J discovery low region for gluino production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Tag bin1 region for gluino production one-step variable-x simplified models

Signal acceptance in SR2J b-Tag bin2 region for gluino production one-step variable-x simplified models

Signal acceptance in SR2J b-Tag bin3 region for gluino production one-step variable-x simplified models

Signal acceptance in SR2J b-Veto bin1 region for gluino production one-step variable-x simplified models

Signal acceptance in SR2J b-Veto bin2 region for gluino production one-step variable-x simplified models

Signal acceptance in SR2J b-Veto bin3 region for gluino production one-step variable-x simplified models

Signal acceptance in SR2J discovery high region for gluino production one-step variable-x simplified models

Signal acceptance in SR2J discovery low region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jhx discovery region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Tag bin1 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Tag bin2 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Tag bin3 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Veto bin1 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Veto bin2 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Veto bin3 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jlx discovery region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Tag bin1 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Tag bin2 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Tag bin3 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Veto bin1 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Veto bin2 region for gluino production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Veto bin3 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J b-Tag bin1 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J b-Tag bin2 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J b-Tag bin3 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J b-Tag bin4 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J b-Veto bin1 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J b-Veto bin2 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J b-Veto bin3 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J b-Veto bin4 region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J discovery high region for gluino production one-step variable-x simplified models

Signal acceptance in SR6J discovery low region for gluino production one-step variable-x simplified models

Signal acceptance in SR2J b-Tag bin1 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Tag bin2 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Tag bin3 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Veto bin1 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Veto bin2 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Veto bin3 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR2J discovery high region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR2J discovery low region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx discovery region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Tag bin1 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Tag bin2 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Tag bin3 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Veto bin1 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Veto bin2 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jhx b-Veto bin3 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx discovery region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Tag bin1 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Tag bin2 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Tag bin3 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Veto bin1 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Veto bin2 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR4Jlx b-Veto bin3 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Tag bin1 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Tag bin2 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Tag bin3 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Tag bin4 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Veto bin1 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Veto bin2 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Veto bin3 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J b-Veto bin4 region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J discovery high region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR6J discovery low region for squark production one-step x = 1/2 simplified models

Signal acceptance in SR2J b-Tag bin1 region for squark production one-step variable-x simplified models

Signal acceptance in SR2J b-Tag bin2 region for squark production one-step variable-x simplified models

Signal acceptance in SR2J b-Tag bin3 region for squark production one-step variable-x simplified models

Signal acceptance in SR2J b-Veto bin1 region for squark production one-step variable-x simplified models

Signal acceptance in SR2J b-Veto bin2 region for squark production one-step variable-x simplified models

Signal acceptance in SR2J b-Veto bin3 region for squark production one-step variable-x simplified models

Signal acceptance in SR2J discovery high region for squark production one-step variable-x simplified models

Signal acceptance in SR2J discovery low region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jhx discovery region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Tag bin1 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Tag bin2 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Tag bin3 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Veto bin1 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Veto bin2 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jhx b-Veto bin3 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jlx discovery region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Tag bin1 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Tag bin2 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Tag bin3 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Veto bin1 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Veto bin2 region for squark production one-step variable-x simplified models

Signal acceptance in SR4Jlx b-Veto bin3 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J b-Tag bin1 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J b-Tag bin2 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J b-Tag bin3 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J b-Tag bin4 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J b-Veto bin1 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J b-Veto bin2 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J b-Veto bin3 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J b-Veto bin4 region for squark production one-step variable-x simplified models

Signal acceptance in SR6J discovery high region for squark production one-step variable-x simplified models

Signal acceptance in SR6J discovery low region for squark production one-step variable-x simplified models

Signal efficiency in SR2J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J discovery high region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J discovery low region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx discovery region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx discovery region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin4 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin1 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin2 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin3 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin4 region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J discovery high region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J discovery low region for gluino production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J discovery high region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J discovery low region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx discovery region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx discovery region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin4 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin1 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin2 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin3 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin4 region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J discovery high region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J discovery low region for gluino production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J discovery high region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J discovery low region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx discovery region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx discovery region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin4 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin1 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin2 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin3 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin4 region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J discovery high region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J discovery low region for squark production one-step x = 1/2 simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J discovery high region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR2J discovery low region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx discovery region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jhx b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx discovery region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR4Jlx b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Tag bin4 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin1 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin2 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin3 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J b-Veto bin4 region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J discovery high region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Signal efficiency in SR6J discovery low region for squark production one-step variable-x simplified models. The -1 value indicates the truth yields for this point is 0 but the reco yields is not 0

Acceptances on TRUTH level of the DMt samples in the tW1L channel for all bins in the SR. The acceptance is defined as the number of weighted TRUTH events in the SR over the number of expected events without any selections. The maps include all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

Efficiencies of the DMt samples in the tW2L SR. The efficiency is defined as the number of weighted reconstructed events over the number of weighted TRUTH events in the SR. The maps include all samples in the $m_a - m_H$ plane with $tan\beta = 1$.

Acceptances on TRUTH level of the DMt samples in the tW2L SR. The acceptance is defined as the number of weighted TRUTH events in the SR over the number of expected events without any selections. The maps include all samples in the $m_a - m_H$ plane with $tan\beta = 1$.

Efficiencies of the DMt samples in the tW2L SR. The efficiency is defined as the number of weighted reconstructed events over the number of weighted TRUTH events in the SR. The maps include all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

Acceptances on TRUTH level of the DMt samples in the tW2L SR. The acceptance is defined as the number of weighted TRUTH events in the SR over the number of expected events without any selections. The maps include all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

Efficiencies of the DMt samples in the tj1L channel for all bins in the SR. The efficiency is defined as the number of weighted reconstructed events over the number of weighted TRUTH events in the SR. The map includes all used samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

Acceptances on TRUTH level of the DMt samples in the tj1L channel for all bins in the SR. The acceptance is defined as the number of weighted TRUTH events in the SR over the number of expected events without any selections. The map includes all used samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tW1L analysis considering only DMt signal.

Upper limits on excluded cross sections of the tW1L analysis considering only the DMt signal.

The expected exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming only $tW$+DM contributions, for the tW1L analysis channel.

The observed exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming only $tW$+DM contributions, for the tW1L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tW1L analysis considering only DMt signal.

Upper limits on excluded cross sections of the tW1L analysis considering only the DMt signal.

The expected exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming only $tW$+DM contributions, for the tW1L analysis channel.

The observed exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming only $tW$+DM contributions, for the tW1L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tW2L analysis considering only DMt signal.

Upper limits on excluded cross sections of the tW2L analysis considering only the DMt signal.

The expected exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming only $tW$+DM contributions, for the tW2L analysis channel.

The observed exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming only $tW$+DM contributions, for the tW2L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tW2L analysis considering only DMt signal.

Upper limits on excluded cross sections of the tW2L analysis considering only the DMt signal.

The expected exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming only $tW$+DM contributions, for the tW2L analysis channel.

The observed exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming only $tW$+DM contributions, for the tW2L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the combined tW1L and tW2L analyses considering only the DMt signal.

Upper limits on excluded cross sections of the combined tW1L and tW2L analyses considering only the DMt signal.

The expected exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming only $tW$+DM contributions, for the statistical combination of the tW1L and tW2L analysis channel.

The observed exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming only $tW$+DM contributions, for the statistical combination of the tW1L and tW2L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the combined tW1L and tW2L analyses considering only the DMt signal.

Upper limits on excluded cross sections of the combined tW1L and tW2L analyses considering only the DMt signal.

The expected exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming only $tW$+DM contributions, for the statistical combination of the tW1L and tW2L analysis channel.

The observed exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming only $tW$+DM contributions, for the statistical combination of the tW1L and tW2L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tW1L analysis considering the DMt$\bar{t}$+DMt signal.

The expected exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the tW1L analysis channel.

The observed exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the tW1L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tW1L analysis considering the DMt$\bar{t}$+DMt signal.

The expected exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the tW1L analysis channel.

The observed exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the tW1L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tW2L analysis considering the DMt$\bar{t}$+DMt signal.

The expected exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the tW2L analysis channel.

The observed exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the tW2L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tW2L analysis considering the DMt$\bar{t}$+DMt signal.

The expected exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the tW2L analysis channel.

The observed exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the tW2L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the combined tW1L and tW2L analyses considering the DMt$\bar{t}$+DMt signal.

The expected exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the statistical combination of the tW1L and tW2L analysis channel.

The observed exclusion contours as a function of $(m_a, m_{H^{\pm}})$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the statistical combination of the tW1L and tW2L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the combined tW1L and tW2L analyses considering the DMt$\bar{t}$+DMt signal.

The expected exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the statistical combination of the tW1L and tW2L analysis channel.

The observed exclusion contours as a function of $(m_{H^{\pm}}, \tan\beta)$, assuming DM$t\bar{t}$ and DM$t$ contributions, for the statistical combination of the tW1L and tW2L analysis channel.

Upper limits on signal strength (excluded cross section over theoretical cross section) of the tj1L analysis considering only the DMt signal.

Upper limits on upper limits on excluded cross sections of the tj1L analysis considering only the DMt signal.

The expected and observed cross section exclusion limits as a function of $m_{H^{\pm}}$ in the tj1L analysis channel for signal models with $m_a = 250~GeV$, and $\tan\beta=0.3$. The $\sigma^{}_\mathrm{BSM}$ is the cross section of the $t$-channel DM production process.

The expected and observed cross section exclusion limits as a function of $m_{H^{\pm}}$ in the tj1L analysis channel for signal models with $m_a = 250~GeV$, and $\tan\beta=0.5$. The $\sigma^{}_\mathrm{BSM}$ is the cross section of the $t$-channel DM production process.

Cross sections of the DMt samples in the tW1L channel. The maps include all samples in the $m_a - m_H$ plane with $tan\beta = 1$.

Cross sections of the DMt samples in the tW1L channel. The maps include all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

Cross sections times branching ratio of the DMt samples in the tW2L channel. The maps include all samples in the $m_a - m_H$ plane with $tan\beta = 1$.

Cross sections times branching ratio of the DMt samples in the tW2L channel. The maps include all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

Cross sections of the DMt samples in the tj1L channel. The map includes all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

MC generator filter efficiencies of the DMt samples in the tW1L channel. The maps include all samples in the $m_a - m_H$ plane with $tan\beta = 1$.

MC generator filter efficiencies of the DMt samples in the tW1L channel. The maps include all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

MC generator filter efficiencies of the DMt samples in the tW2L channel. The maps include all samples in the $m_a - m_H$ plane with $tan\beta = 1$.

MC generator filter efficiencies of the DMt samples in the tW2L channel. The maps include all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

MC generator filter efficiencies of the DMt samples in the tj1L channel. The map includes all samples in the $m_H - tan\beta$ plane with $m_a = 250~GeV$.

Background-only fit results for the tW1L and tW2L signal regions. The backgrounds which contribute only a small amount (rare processes such as triboson, Higgs boson production processes, $t\bar{t}t\bar{t}$, $t\bar{t}WW$ and non-prompt or misidentified leptons background) are grouped and labelled as ``Others´´. The quoted uncertainties on the fitted SM background include both the statistical and systematic uncertainties.

Background-only fit results for the tj1L signal regions. The backgrounds which contribute only a small amount ($Z$+jets, rare processes such as $tWZ$, triboson, Higgs boson production processes, ,$t\bar{t}t\bar{t}$, $t\bar{t}WW$) are grouped and labelled as ``Others´´. The quoted uncertainties on the fitted SM background include both the statistical and systematic uncertainties.

Cutflow of the weighted events with statistical uncertainties for two DMt samples in all bins of the tW1L channel. The PreSelection includes at least 1 lepton in the event, at least 1 $b$-jet with $p_{\mathrm{T}} > 50~GeV$, $m\mathrm{_{T}^{lep}} > 30~GeV$, $\Delta\phi\mathrm{_{4jets, MET}^{min}} > 0.5$ and $E\mathrm{_{T}^{miss}} > 200~GeV$.

Cutflow of the weighted events with statistical uncertainties for two DMt samples in the tW2L channel. The PreSelection includes at least 2 leptons in the event, at least 1 $b$-jet with $p_{\mathrm{T}} > 40~GeV$, $m_{ll} > 40~GeV$, $m\mathrm{_{T2}} > 40~GeV$, $\Delta\phi\mathrm{_{4jets, MET}^{min}} > 0.5$ and $E\mathrm{_{T}^{miss}} > 200~GeV$.

Cutflow of the weighted events with the statistical uncertainties (except for the first cuts) for two DMt samples in all bins off the tj1L channel. The PreSelection includes at least 1 lepton in the event, at least 1 $b$-jet with $p_{\mathrm{T}} > 50~GeV$, $m\mathrm{_{T}^{lep}} > 30~GeV$, $\Delta\phi\mathrm{_{4jets, MET}^{min}} > 0.5$ and $E\mathrm{_{T}^{miss}} > 200~GeV$.

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, where all slepton flavors are mass degenerate (co-NLSP).

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, for the selectron signal model. Selectron ($\tilde{e}_{L, R}$) refers to the scalar superpartners of left- and right-handed electrons.

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, for the superpartners of the left-handed electrons, $\tilde{e}_L$.

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, for the superpartners of the right-handed electrons, $\tilde{e}_R$.

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, for the smuon signal model. Smuon ($\tilde{\mu}_{L, R}$) refers to the scalar superpartners of left- and right-handed muons.

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, for the superpartners of the left-handed muons, $\tilde{\mu}_L$.

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, for the superpartners of the right-handed muons, $\tilde{\mu}_R$.

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, for the stau signal model. Staus, $\tilde{\tau}_{1,2}$ are the mixed states of the superpartners of the left- and right-handed $\tau$ leptons, with mixing angle $\sin\theta_{\tilde\tau}=0.95$.

Upper limits on observed signal cross section, $\sigma_\text{obs}^\text{95}$, for $\tilde{\tau}_L$ production, where $\tilde{\tau}_L$ is pure-state superpartner of the left-handed $\tau$.

The expected and observed yields in the signal regions. Combined statistical and systematic uncertainties are presented. Estimates are truncated at 0 if the size of measured systematic uncertainties would yield a negative result.

Reconstruction efficiency as a function of $|d_{0}|$ and $p_\text{T}$ for signal electrons. Monte Carlo samples with $\tilde{e}$ or $\tilde{\mu}$ with mass 400 GeV and 1 ns lifetime were used. Efficiency is defined as the number of leptons passing all signal requirements and matched to generator-level muons divided by the number of generator level leptons with $p_\text{T} > 65$ GeV, $|d_{0}| >$ 3 mm, and |$\eta$| $<$ 2.47 for electrons. No event-level selections are made. Reconstructed leptons must pass all quality criteria, including the cosmic veto. Electron selection scale factors are included on the reconstructed leptons. The overflow is not shown in these plots.

Reconstruction efficiency as a function of $|d_{0}|$ and $p_\text{T}$ for signal muons. Monte Carlo samples with $\tilde{e}$ or $\tilde{\mu}$ with mass 400 GeV and 1 ns lifetime were used. Efficiency is defined as the number of leptons passing all signal requirements and matched to generator-level muons divided by the number of generator level leptons with $p_\text{T} > 65$ GeV, $|d_{0}| >$ 3 mm, and |$\eta$| $<$ 2.5 for muons. No event-level selections are made. Reconstructed leptons must pass all quality criteria, including the cosmic veto. Muon selection scale factors are included on the reconstructed leptons. The overflow is not shown in these plots.

Acceptance for $\tilde{e}$ of various masses and lifetimes in SR-$ee$. Acceptance is 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. To be accepted, events are required to have at least 2 truth electrons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.47, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$.

Acceptance for $\tilde{\mu}$ of various masses and lifetimes in SR-$\mu\mu$. Acceptance is 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. To be accepted, events are required to have at least 2 truth muons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.5, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$.

Efficiency for $\tilde{e}$ of various masses and lifetimes in SR-$ee$. Efficiency is defined as the number of selected reconstruced events divided by the acceptance. To be accepted, events are required to have at least 2 truth electrons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.47, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$. To be selected, events must satisfy all signal region requirements.

Efficiency for $\tilde{\mu}$ of various masses and lifetimes in SR-$\mu\mu$. Efficiency is defined as the number of selected reconstruced events divided by the acceptance. To be accepted, events are required to have at least 2 truth muons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.5, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$. To be selected, events must satisfy all signal region requirements.

Acceptance as a function of the generator-level $p_\text{T}$ of the leading and subleading lepton in SR-$ee$ from $\tilde{e}$ decays. The plot is made from signal Monte Carlo events with $\tilde{\ell}$ with mass of 400 GeV and lifetime of 1 ns. To be accepted, events are required to have at least 2 truth leptons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.47 for electrons, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$. The overflow is not shown in these plots.

Acceptance as a function of the generator-level $p_\text{T}$ of the leading and subleading lepton in SR-$\mu\mu$ from $\tilde{\mu}$ decays. The plot is made from signal Monte Carlo events with $\tilde{\ell}$ with mass of 400 GeV and lifetime of 1 ns. To be accepted, events are required to have at least 2 truth leptons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.5 for muons, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$. The overflow is not shown in these plots.

Acceptance for $\tilde{\tau}$ of various masses and lifetimes in SR-$ee$. Acceptance is 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. To be accepted, events are required to have at least 2 truth electrons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.47, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$.

Acceptance for $\tilde{\tau}$ of various masses and lifetimes in SR-$e\mu$. Acceptance is 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. To be accepted, events are required to have at least 2 truth leptons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.5 (2.47) for muons (electrons), $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$.

Acceptance for $\tilde{\tau}$ of various masses and lifetimes in SR-$\mu\mu$. Acceptance is 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. To be accepted, events are required to have at least 2 truth muons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.5, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$.

Efficiency for $\tilde{\tau}$ of various masses and lifetimes in SR-$ee$. Efficiency is defined as the number of selected reconstruced events divided by the acceptance. To be accepted, events are required to have at least 2 truth electrons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.47, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$. To be selected, events must satisfy all signal region requirements.

Efficiency for $\tilde{\tau}$ of various masses and lifetimes in SR-$e\mu$. Efficiency is defined as the number of selected reconstruced events divided by the acceptance. To be accepted, events are required to have at least 2 truth leptons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.5 (2.47) for muons (electrons), $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$. To be selected, events must satisfy all signal region requirements.

Efficiency for $\tilde{\tau}$ of various masses and lifetimes in SR-$\mu\mu$. Efficiency is defined as the number of selected reconstruced events divided by the acceptance. To be accepted, events are required to have at least 2 truth muons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.5, $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$. To be selected, events must satisfy all signal region requirements.

Acceptance as a function of the generator-level $p_\text{T}$ of the leading and subleading lepton in SR-$e\mu$ from $\tilde{\tau}$ decays. The plot is made from signal Monte Carlo events with $\tilde{\ell}$ with mass of 400 GeV and lifetime of 1 ns. To be accepted, events are required to have at least 2 truth leptons with $p_\text{T} > 65$ GeV, |$\eta$| $<$ 2.5 (2.47) for muons (electrons), $|d_{0}| > 3$ mm, and $\Delta R_{\ell\ell} > 0.2$. Events are also required to fall into one of the acceptance regions of the triggers used. At generator level, events must have one of the following: one electron with $p_\text{T} >$ 160 GeV, 2 electrons each with $p_\text{T} >$ 60 GeV, or 1 muon with $p_\text{T} >$ 60 GeV and $|\eta| < 1.07$. The overflow is not shown in these plots.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane where all slepton flavors and chiralities are mass degenerate.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane where all slepton flavors and chiralities are mass degenerate.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$ee$ targeting selectron production. Selectrons ($\tilde{e}_{L, R}$) refer to the scalar superpartners of left- and right-handed electrons, which are assumed to be mass degenerate.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$ee$ targeting selectron production. Selectrons ($\tilde{e}_{L, R}$) refer to the scalar superpartners of left- and right-handed electrons, which are assumed to be mass degenerate.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$ee$ targeting selectron production. Selectrons ($\tilde{e}_{L}$) refer to the scalar superpartners of left-handed electrons.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$ee$ targeting selectron production. Selectrons ($\tilde{e}_{L}$) refer to the scalar superpartners of left-handed electrons.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$ee$ targeting selectron production. Selectrons ($\tilde{e}_{R}$) refer to the scalar superpartners of right-handed electrons. Purple denotes the region excluded by LEP.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$ee$ targeting selectron production. Selectrons ($\tilde{e}_{R}$) refer to the scalar superpartners of right-handed electrons. Purple denotes the region excluded by LEP.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$\mu\mu$ targeting smuon production. Smuons ($\tilde{\mu}_{L, R}$) refer to the scalar superpartners of left- and right-handed muons, which are assumed to be mass degenerate.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$\mu\mu$ targeting smuon production. Smuons ($\tilde{\mu}_{L, R}$) refer to the scalar superpartners of left- and right-handed muons, which are assumed to be mass degenerate.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$\mu\mu$ targeting smuon production. Smuons ($\tilde{\mu}_{L}$) refer to the scalar superpartners of left-handed muons.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$\mu\mu$ targeting smuon production. Smuons ($\tilde{\mu}_{L}$) refer to the scalar superpartners of left-handed muons.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$\mu\mu$ targeting smuon production. Smuons ($\tilde{\mu}_{R}$) refer to the scalar superpartners of right-handed muons. Purple denotes the region excluded by LEP.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\ell})$ plane in SR-$\mu\mu$ targeting smuon production. Smuons ($\tilde{\mu}_{R}$) refer to the scalar superpartners of right-handed muons. Purple denotes the region excluded by LEP.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\tau})$ plane. Staus, $\tilde{\tau}_{1,2}$ are the mixed states of the superpartners of the left- and right-handed $\tau$ leptons, with mixing angle $\sin(\theta_{\tilde\tau})=0.95$, which are assumed to be mass degenerate.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\tau})$ plane. Staus, $\tilde{\tau}_{1,2}$ are the mixed states of the superpartners of the left- and right-handed $\tau$ leptons, with mixing angle $\sin(\theta_{\tilde\tau})=0.95$, which are assumed to be mass degenerate.

Observed 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\tau}_L)$ plane, where $\tilde{\tau}_L$ is the pure-state super-partner of the left-handed $\tau$. Purple denotes the region excluded by LEP. This result does not present signficant sensitivity to the pure-state superpartner of the right-handed $\tau$.

Expected 95% CL exclusion sensitivity. The limit is displayed in the lifetime vs. $m(\tilde{\tau}_L)$ plane, where $\tilde{\tau}_L$ is the pure-state super-partner of the left-handed $\tau$. Purple denotes the region excluded by LEP. This result does not present signficant sensitivity to the pure-state superpartner of the right-handed $\tau$.