List of relative uncertainties of the measured inclusive $t\bar{t}\text{Z}$ cross section from the combined fit. The uncertainties are symmetrised for presentation and grouped into the categories described in the text. The quadratic sum of the individual uncertainties is not equal to the total uncertainty due to correlations introduced by the fit.

The definitions of the trilepton signal regions: for the inclusive measurement, a combination of the regions with pseudo-continuous $b$-tagging 3$\ell$-Z-1$b$4$j$-PCBT and 3$\ell$-Z-2$b$3$j$-PCBT is used, whereas for the differential measurement, only the region 3$\ell$-Z-2$b$3$j$, with a fixed $b$-tagging WP is employed.

The definitions of the trilepton signal regions: for the inclusive measurement, a combination of the regions with pseudo-continuous $b$-tagging 3$\ell$-Z-1$b$4$j$-PCBT and 3$\ell$-Z-2$b$3$j$-PCBT is used, whereas for the differential measurement, only the region 3$\ell$-Z-2$b$3$j$, with a fixed $b$-tagging WP is employed.

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 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 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 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 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 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 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 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 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 $p_{\textrm{T}}^{l \textrm{non-}Z}$ 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 $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta y (Z, t_{\textrm{lep}})|$ 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 $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ 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 $|\Delta \phi (t\bar{t}, Z)|/\pi$ 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 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 $p_{\textrm{T}}^{l \textrm{non-}Z}$ 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 $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta y (Z, t_{\textrm{lep}})|$ 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 $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ 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 $|\Delta \phi (t\bar{t}, Z)|/\pi$ 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 $p_{\textrm{T}}^{t\bar{t}}$ 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 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 $p_{\textrm{T}}^{l \textrm{non-}Z}$ 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 $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta y (Z, t_{\textrm{lep}})|$ 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 $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ 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 $|\Delta \phi (t\bar{t}, Z)|/\pi$ 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 $p_{\textrm{T}}^{t\bar{t}}$ 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 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 $p_{\textrm{T}}^{l \textrm{non-}Z}$ 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 $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta y (Z, t_{\textrm{lep}})|$ 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 $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ 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 $|\Delta \phi (t\bar{t}, Z)|/\pi$ 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 $p_{\textrm{T}}^{t\bar{t}}$ 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 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 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 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 $p_{\textrm{T}}$ 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 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 $p_{\textrm{T}}^{l \textrm{non-}Z}$ 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 $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ 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 $|\Delta \phi (t\bar{t}, Z)|/\pi$ 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 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 $p_{\textrm{T}}$ 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 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 $p_{\textrm{T}}^{l \textrm{non-}Z}$ 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 $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ 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 $|\Delta \phi (t\bar{t}, Z)|/\pi$ 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 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 $p_{\textrm{T}}$ 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 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 $p_{\textrm{T}}^{l \textrm{non-}Z}$ 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 $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ 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 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 $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ 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 $|\Delta \phi (t\bar{t}, Z)|/\pi$ 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 $p_{\textrm{T}}^{t\bar{t}}$ 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 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 $p_{\textrm{T}}$ 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 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 $p_{\textrm{T}}^{l \textrm{non-}Z}$ 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 $|\Delta \phi (Z, t_{\textrm{lep}})|/\pi$ 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 $|\Delta y (Z, t_{\textrm{lep}})|/\pi$ 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 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 $|\Delta \phi (l_{t}^{+}, l_{\bar{t}}^{-})|/\pi$ 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 $|\Delta \phi (t\bar{t}, Z)|/\pi$ 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 $p_{\textrm{T}}^{t\bar{t}}$ 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 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.

The +1 $\sigma$ band of the observed exclusion contor for the $Z^{\prime}_{B}$ model in the $m_{\chi}$-$m_{Z^{\prime}_{B}}$ plane.

The -1 $\sigma$ band of the observed exclusion contor for the $Z^{\prime}_{B}$ model in the $m_{\chi}$-$m_{Z^{\prime}_{B}}$ plane.

A comparison of the inferred limits to the constraints from direct detection experiments on the spin-independent DM--nucleon cross section in the context of the $Z'_B$ simplified model with vector couplings. Limits are shown at 90% CL.

The observed exclusion contor for the $Z^{\prime}$-2HDM model in the $m_{A}$-$m_{Z^{\prime}}$ plane.

The expected exclusion contor for the $Z^{\prime}$-2HDM model in the $m_{A}$-$m_{Z^{\prime}}$ plane.

The +1 $\sigma$ band of the observed exclusion contor for the $m_{A}$-$Z^{\prime}$-2HDM model in the $m_{Z^{\prime}}$ plane.

The -1 $\sigma$ band of the observed exclusion contor for the $m_{A}$-$Z^{\prime}$-2HDM model in the $m_{Z^{\prime}}$ plane.

The observed exclusion contor for the 2HDM-a model in the $m_{A}$-$m_{a}$ plane.

The expected exclusion contor for the 2HDM-a model in the $m_{A}$-$m_{a}$ plane.

The +1 $\sigma$ band of the observed exclusion contor for the 2HDM-a model in the $m_{A}$-$m_{a}$ plane.

The -1 $\sigma$ band of the observed exclusion contor for the 2HDM-a model in the $m_{A}$-$m_{a}$ plane.

The observed exclusion contor for the 2HDM-a model in the $tan\beta$-$m_{a}$ plane.

The expected exclusion contor for the 2HDM-a model in the $tan\beta$-$m_{a}$ plane.

The +1 $\sigma$ band of the observed exclusion contor for the 2HDM-a model in the $tan\beta$-$m_{a}$ plane.

The -1 $\sigma$ band of the observed exclusion contor for the 2HDM-a model in the $tan\beta$-$m_{a}$ plane.

The exclusion limits at 95% CL for the 2HDM+a model as a function of $\sin \theta$ for $m_{A,H^{\pm},H}$= 600GeV, $m_a$ = 200GeV, $\tan \beta$ = 1.0$.

The exclusion limits at 95% CL for the 2HDM+a model as a function of $\sin \theta$ for $m_{A,H^{\pm},H}$= 1000GeV, $m_a$ = 350GeV, $\tan \beta$ = 1.0$.

Breakdown of the dominant systematic uncertainties.

Event yields in the range of 120 $<m_{\gamma\gamma}<$ 130 GeV for data, signal models, the SM Higgs boson background and non-resonant background in each analysis category, for an integrated luminosity of $139$fb$^{-1}$.

Detailed background contributions from the SM Higgs boson and continuum background for each cut

Detailed contributions from the signals for each cut.

Acceptance times efficiency for several signals in each category.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be zero.

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) is taken to be R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE ZERO.

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).

R=SIG(L)/SIG(T) IS TAKEN TO BE R(QCD).