Number of predicted signal events in the $\mu\mu X$ final states, for several benchmark signal hypotheses for Majorana HNL. The quoted uncertainties include statistical and systematic uncertainties.

The 95% CL limits on $|V_{Ne}|^2$ as a function of the HNL mass for a Majorana HNL. Values of $-1$ indicate that no limit is available for the mass point.

The 95% CL limits on $|V_{N\mu}|^2$ as a function of the HNL mass for a Majorana HNL. Values of $-1$ indicate that no limit is available for the mass point.

The 95% CL limits on $|V_{Ne}|^2$ as a function of the HNL mass for a Dirac HNL. Values of $-1$ indicate that no limit is available for the mass point.

The 95% CL limits on $|V_{N\mu}|^2$ as a function of the HNL mass for a Dirac HNL. Values of $-1$ indicate that no limit is available for the mass point.

Covariance matrix for the background prediction in the signal region bins for the $eeX$ final states. The bin labels specify the channel, the $m(\ell_2,\ell_3)$ bin, and the $\Delta_{\mathrm{2D}}$ bin.

Covariance matrix for the background prediction in the signal region bins for the $\mu\mu X$ final states. The bin labels specify the channel, the $m(\ell_2,\ell_3)$ bin, and the $\Delta_{\mathrm{2D}}$ bin.

Correlation matrix for the background prediction the signal region bins for the $eeX$ final states. The bin labels specify the channel, the $m(\ell_2,\ell_3)$ bin, and the $\Delta_{\mathrm{2D}}$ bin.

Correlation matrix for the background prediction the signal region bins for the $\mu\mu X$ final states. The bin labels specify the channel, the $m(\ell_2,\ell_3)$ bin, and the $\Delta_{\mathrm{2D}}$ bin.

Mean lifetime and production cross section for HNLs of Majorana nature, assuming the decay to three charged leptons and coupling to only electrons or muons, as evaluated from simulated event samples.

Expected and observed yields from the standard model processeses in the WW signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WW signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WW signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WZ signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WZ signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WZ signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

The measured inclusive fiducial cross section measurements. The WW fiducial region is defined by requiring two same-sign leptons with $p_{T}>20$, $|\eta|<2.5$, and $m_{ll}>20$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$. The jets at generator level are clustered from stable particles, excluding neutrinos, using the anti-kt clustering algorithm with R = 0.4, and are required to have $p_{T}>50$ and $|\eta|<4.7$. The jets within $\Delta R<0.4$ of the selected charged leptons are not included. The WZ fiducial region is defined by requiring three leptons with $p_{T}>20$, $|\eta|<2.5$, a pair of opposite charge same-flavor lepton pair with $|m_{ll}-m_{Z}|<15$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$.

The measured inclusive fiducial cross section measurements. The WW fiducial region is defined by requiring two same-sign leptons with $p_{T}>20$, $|\eta|<2.5$, and $m_{ll}>20$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$. The jets at generator level are clustered from stable particles, excluding neutrinos, using the anti-kt clustering algorithm with R = 0.4, and are required to have $p_{T}>50$ and $|\eta|<4.7$. The jets within $\Delta R<0.4$ of the selected charged leptons are not included. The WZ fiducial region is defined by requiring three leptons with $p_{T}>20$, $|\eta|<2.5$, a pair of opposite charge same-flavor lepton pair with $|m_{ll}-m_{Z}|<15$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$.

The measured inclusive fiducial cross section measurements. The WW fiducial region is defined by requiring two same-sign leptons with $p_{T}>20$, $|\eta|<2.5$, and $m_{ll}>20$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$. The jets at generator level are clustered from stable particles, excluding neutrinos, using the anti-kt clustering algorithm with R = 0.4, and are required to have $p_{T}>50$ and $|\eta|<4.7$. The jets within $\Delta R<0.4$ of the selected charged leptons are not included. The WZ fiducial region is defined by requiring three leptons with $p_{T}>20$, $|\eta|<2.5$, a pair of opposite charge same-flavor lepton pair with $|m_{ll}-m_{Z}|<15$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$.

Distributions of $m_{jj}$ in the WW signal region.

Distributions of $m_{jj}$ in the WW signal region.

Distributions of $m_{jj}$ in the WW signal region.

Distributions of $m_{ll}$ in the WW signal region.

Distributions of $m_{ll}$ in the WW signal region.

Distributions of $m_{ll}$ in the WW signal region.

Distributions of $m_{jj}$ in the WZ signal region.

Distributions of $m_{jj}$ in the WZ signal region.

Distributions of $m_{jj}$ in the WZ signal region.

Distributions of BDT score in the WZ signal region.

Distributions of BDT score in the WZ signal region.

Distributions of BDT score in the WZ signal region.

Absolute WW cross section in $m_{jj}$ bins.

Absolute WW cross section in $m_{jj}$ bins.

Absolute WW cross section in $m_{jj}$ bins.

Normalized WW cross section in $m_{jj}$ bins.

Normalized WW cross section in $m_{jj}$ bins.

Normalized WW cross section in $m_{jj}$ bins.

Absolute WW cross section in $m_{ll}$ bins.

Absolute WW cross section in $m_{ll}$ bins.

Absolute WW cross section in $m_{ll}$ bins.

Normalized WW cross section in $m_{ll}$ bins.

Normalized WW cross section in $m_{ll}$ bins.

Normalized WW cross section in $m_{ll}$ bins.

Absolute WW cross section in $p_{T}^{l max}$ bins.

Absolute WW cross section in $p_{T}^{l max}$ bins.

Absolute WW cross section in $p_{T}^{l max}$ bins.

Normalized WW cross section in $p_{T}^{l max}$ bins.

Normalized WW cross section in $p_{T}^{l max}$ bins.

Normalized WW cross section in $p_{T}^{l max}$ bins.

Absolute WZ cross section in $m_{jj}$ bins.

Absolute WZ cross section in $m_{jj}$ bins.

Absolute WZ cross section in $m_{jj}$ bins.

Normalized WZ cross section in $m_{jj}$ bins.

Normalized WZ cross section in $m_{jj}$ bins.

Normalized WZ cross section in $m_{jj}$ bins.

Distributions of $m_{T}^{WW}$ in the WW signal region.

Distributions of $m_{T}^{WW}$ in the WW signal region.

Distributions of $m_{T}^{WW}$ in the WW signal region.

Distributions of $m_{T}^{WZ}$ in the WZ signal region.

Distributions of $m_{T}^{WZ}$ in the WZ signal region.

Distributions of $m_{T}^{WZ}$ in the WZ signal region.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic, obtained without using any unitarization procedure.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic, obtained without using any unitarization procedure.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic, obtained without using any unitarization procedure.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic by cutting the EFT expansion at the unitarity limit.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic by cutting the EFT expansion at the unitarity limit.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic by cutting the EFT expansion at the unitarity limit.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the $\text{t}\bar{\text{t}}$ CR. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the low-MET bin for the WZ-enriched region. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the WZ-enriched region. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the SS CR. Uncertainties include both the statistical and systematic components.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the med-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the ultra-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 3$\ell$-Ewk search regions: the post-fit distribution of the $M^{\text{min}}_{\text{SFOS}}(\ell\ell)$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 3$\ell$-Ewk search regions: the post-fit distribution of the $M^{\text{min}}_{\text{SFOS}}(\ell\ell)$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the med-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the ultra-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the TChiWZ model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. Results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2} \widetilde{m}_{\tilde{\chi}^0_1} > 0$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the TChiWZ model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. Results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2} \widetilde{m}_{\tilde{\chi}^0_1} < 0$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the simplified higgsino model. The simplified model includes both neutralino pair and neutralino-chargino production modes. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the pMSSM higgsino model. The pMSSM one includes all possible production modes. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the T2bff$\tilde{\chi}^0_1$ simplified model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the T2bW simplified model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.

Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.

Product of efficiency and acceptance of the event selection for T signal events as a function of the particle mass $m_\mathrm{T}$ and width $\Gamma$ for the different hypotheses considered.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with 0 forward jets. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved validation region with at least 1 forward jet. Data are compared to backgrounds are after the data-driven extraction is performed.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the partially merged region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with 0 forward jets. Data are compared to backgrounds obtained post-fit.

Distribution of the transverse mass $M_T$ reconstructed from the top quark and the missing transverse energy in the resolved region with at least 1 forward jet. Data are compared to backgrounds obtained post-fit.

Observed and expected 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a hadronic top quark and a Z boson to neutrinos as a function of the T mass, for a T with a width of 0.01 $ imes$ its mass.

Observed and expected 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a hadronic top quark and a Z boson to neutrinos as a function of the T mass, for a T with a width of 0.1 $ imes$ its mass.

Observed and expected 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a hadronic top quark and a Z boson to neutrinos as a function of the T mass, for a T with a width of 0.2 $ imes$ its mass.

Observed and expected 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a hadronic top quark and a Z boson to neutrinos as a function of the T mass, for a T with a width of 0.3 $ imes$ its mass.

Model independent observed 95% CL upper limits on the product of the cross section and branching fraction of a T decaying to a top quark and a Z boson as a function of the T mass and width.

Singlet model 95% CL excluded values of the product of the cross section and branching fraction for a T decaying to a top quark and a Z boson.

Distribution of the three leading leptons flavour in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

Efficiency, acceptance, and proportion of events with leptonic tau decays in WZ production

WZ fiducial cross section in the four flavour exclusive and the flavour inclusive channels

WZ total cross section extrapolated from the four flavour exclusive and the flavour inclusive channels

Distribution of the total lepton charge in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

W$^{+}$Z fiducial cross section in the four flavour exclusive and the flavour inclusive channels

W$^{-}$Z fiducial cross section in the four flavour exclusive and the flavour inclusive channels

WZ charge asymmetry ratio measured on each of the four flavour exclusive and the flavour inclusive channels

Distribution of the cosine of the W polarization angle times total lepton charge in the SR-WZ with uncertainties evaluated after the W polarization fit

Distribution of the cosine of the Z polarization angle in the SR-WZ with uncertainties evaluated after the Z polarization fit

Best fits to the W and Z polarization fractions

2D confidence regions at the 68, 95, and 99% CL in the $f_O^W$-$f_{L}^W-f_R^W$ plane

2D confidence regions at the 68, 95, and 99% CL in the $f_O^Z$-$f_{L}^Z-f_R^Z$ plane

Distribution of the invariant mass of the WZ system in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

Best fit values and one dimensional confidence regions in several EFT coefficients obtained from the EFT fit considering both the SM interferences and purely BSM (order $\Lambda^{-2}$ and $\Lambda^{-4}$) terms

2D confidence regions at the 68, 95, and 99% CL in the $c_{www}$-$c_{w}$ plane

2D confidence regions at the 68, 95, and 99% CL in the $c_{w}$-$c_{b}$ plane

2D confidence regions at the 68, 95, and 99% CL in the $c_{www}$-$c_{w}$ plane

Best fit values and one dimensional confidence regions in several EFT coefficients obtained from the EFT fit considering only the SM-EFT interference (order $\Lambda^{-2}$) terms

Evolution of the best fit and expected and observed 95% CI for the $c_{w}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $c_{b}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $c_{www}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $\tilde{c}_{www}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $\tilde{c}_{w}$ parameter as a function of the cutoff scale

Differential cross section with respect to the transverse momentum of the Z boson

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the Z boson

Response matrix for the $p_{T}$ of the Z boson obtained with POWHEG

Differential cross section with respect to the transverse momentum of the leading jet

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the leading jet

Response matrix for the $p_{T}$ of the leading jet obtained with POWHEG

Differential cross section with respect to the jet multiplicity

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the jet multiplicity

Response matrix for the jet multiplicity obtained with POWHEG

Differential cross section with respect to the invariant mass of the WZ system

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the invariant mass of the WZ system

Response matrix for the invariant mass of the WZ system obtained with POWHEG

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson

Response matrix for the $p_{T}$ of the lepton associated to the W boson obtained with POWHEG

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson, W$^{+}$Z only

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson, W$^{-}$Z only

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge

Response matrix for the cosine of the W polarization angle times total lepton charge obtained with POWHEG

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge, W$^{+}$Z only

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge, W$^{-}$Z only

Differential cross section with respect to the cosine of the Z polarization angle

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle

Response matrix for the cosine of the Z polarization angle obtained with POWHEG

Differential cross section with respect to the cosine of the Z polarization angle, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle, W$^{+}$Z only

Differential cross section with respect to the cosine of the Z polarization angle, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle, W$^{-}$Z only

Distribution of $m_{\mathrm{jjj}}$ for preselected events with $\mathrm{N}_{j}$ = 3

Distribution of $m_{\mathrm{j}}$ for preselected events with $\mathrm{N}_{j}$ = 3

Distribution of the deep-WH value of the highest-mass jet with 60 < $m_{\mathrm{j}}$ < 100 GeV for preselected events with $\mathrm{N}_{j}$ = 3

scale factors (SFs) for W, $t^{2}$, and q/g matched jets in the low-$m_{\mathrm{j}}$ and low-$p_{\mathrm{T}}$ (LL) bin, as functions of the deep-W discriminant value.

scale factors (SFs) for W, $t^{2}$, and q/g matched jets in the low-$m_{\mathrm{j}}$ and high-$p_{\mathrm{T}}$ (LH) bin, as functions of the deep-W discriminant value.

scale factors (SFs) for $t^{2}$, $t^{3,4}$, and q/g matched jets in the high-$m_{\mathrm{j}}$ and low-$p_{\mathrm{T}}$ (HL) bin, as functions of the deep-WH discriminant value.

scale factors (SFs) for $t^{2}$, $t^{3,4}$, and q/g matched jets in the high-$m_{\mathrm{j}}$ and high-$p_{\mathrm{T}}$ (HH) bin, as functions of the deep-WH discriminant value.

The deep-W discriminant of the jet with highest mass in the single-lepton sideband for LL samples.

The deep-W discriminant of the jet with highest mass in the single-lepton sideband for LH samples.

The deep-WH discriminant of the jet with highest mass in the single-lepton sideband for HL samples.

The deep-WH discriminant of the jet with highest mass in the single-lepton sideband for HH samples.

Comparison of the distribution of data and simulated backgrounds, as a function of the deep-W discriminant value for the highest-mass jet in CR1, after the scale factors have been applied.

Comparison of the distribution of data and simulated backgrounds, as a function of the deep-WH discriminant value for the highest-mass jet in CR2, after the scale factors have been applied.

Comparison of the distribution of data and simulated backgrounds, as a function of the deep-WH discriminant value for the highest-mass jet in CR3, after the scale factors have been applied.

Comparison of the distribution of data and simulated backgrounds, as a function of the deep-W discriminant value for the highest-mass jet in CR45, after the scale factors have been applied.

Comparison of the distribution of data and simulated backgrounds, as a function of the deep-W discriminant value for the highest-mass jet in CR6, after the scale factors have been applied.

The $m_{\mathrm{j}^{\mathrm{max}}}$ distributions for different jet types for SR1–3 events of the signal with $m_{\mathrm{W}_{\mathrm{KK}}}$ = 2.5 TeV, $m_{\mathrm{R}}$ = 0.2 TeV$ without deep-W (WH) constraints.

The deep-W distribution normalized to unity for the shown components of of the signal with $m_{\mathrm{W}_{\mathrm{KK}}}$ = 2.5 TeV, $m_{\mathrm{R}}$ = 0.2 TeV. The $t^{3,4}$ jets from the preselected sample, normalized to unity, are superimposed to compare shapes with the $R^{3q}$ and $R^{4q}$ distributions.

The deep-WH distribution normalized to unity for the shown components of of the signal with $m_{\mathrm{W}_{\mathrm{KK}}}$ = 2.5 TeV, $m_{\mathrm{R}}$ = 0.2 TeV. The $t^{3,4}$ jets from the preselected sample, normalized to unity, are superimposed to compare shapes with the $R^{3q}$ and $R^{4q}$ distributions.

The $m_{\mathrm{jj}}$ distribution for CR1 for data and simulation.

The $m_{\mathrm{jj}}$ distribution for CR2 for data and simulation.

The $m_{\mathrm{jj}}$ distribution for CR3 for data and simulation.

The $m_{\mathrm{jjj}}$ distribution for CR45 for data and simulation.

The $m_{\mathrm{jjj}}$ distribution for CR6 for data and simulation.

Post-fit distributions of the reconstructed triboson system ($m_{\mathrm{jj}}$) in data and simulation for SR1.

Post-fit distributions of the reconstructed triboson system ($m_{\mathrm{jj}}$) in data and simulation for SR2.

Post-fit distributions of the reconstructed triboson system ($m_{\mathrm{jj}}$) in data and simulation for SR3.

Post-fit distributions of the reconstructed triboson system ($m_{\mathrm{jjj}}$) in data and simulation for SR4.

Post-fit distributions of the reconstructed triboson system ($m_{\mathrm{jjj}}$) in data and simulation for SR5.

Post-fit distributions of the reconstructed triboson system ($m_{\mathrm{jjj}}$) in data and simulation for SR6.

Observed upper limits at $95\%$ CL on the signal cross section $\times$ branching fraction as functions of the $m_{\mathrm{W}_{\mathrm{KK}}}$ and $m_{\mathrm{R}}$ resonance masses.

Expected median lower limit contour on the $m_{\mathrm{W}_{\mathrm{KK}}}$ and $m_{\mathrm{R}}$ plane.

Expected $+ 1$ s.d. lower limit contour on the $m_{\mathrm{W}_{\mathrm{KK}}}$ and $m_{\mathrm{R}}$ plane.

Expected - 1 s.d. lower limit contour on the $m_{\mathrm{W}_{\mathrm{KK}}}$ and $m_{\mathrm{R}}$ plane.

Observed lower limit contour on the $m_{\mathrm{W}_{\mathrm{KK}}}$ and $m_{\mathrm{R}}$ plane.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

Normalized differential cross section distribution as a function of the leading b jet transverse momentum for the Z + >= 1 b jet events

Differential cross section distribution as a function of the leading b jet absolute pseudorapidity for the Z + >= 1 b jet events

Normalized differential cross section distribution as a function of the leading b jet absolute pseudorapidity for the Z + >= 1 b jet events

Differential cross section distribution as a function of the leading b jet transverse momentum for Z +>= 1 b jet events

Normalized differential cross section distribution as a function of azimuthal difference between Z boson and the leading b jet for the Z + >= 1 b jet events

Differential cross section distribution as a function of the rapidity difference between Z boson and the leading b jet for the Z + >= 1 b jet events

Normalized differential cross section distribution as a function of the rapidity difference between Z boson and the leading b jet for the Z + >= 1 b jet events

Differential cross section distribution as a function of the angular separation between the Z boson and the leading b jet for the Z + >= 1 b jet events

Normalized differential cross section distribution as a function of the angular separation between the Z boson and the leading b jet for the Z + >= 1 b jet events

Differential cross section distribution as a function of the leading b jet transverse momentum for the Z + >= 2 b jet events

Normalized differential cross section distribution as a function of the leading b jet transverse momentum for the Z + >= 2 b jet events

Differential cross section distribution as a function of the leading b jet absolute pseudorapidity

Normalized differential cross section distribution as a function of the leading b jet absolute pseudorapidity

Differential cross section distribution as a function of the subleading b jet transverse momentum for the Z + >= 2 b jet events

Normalized differential cross section distribution as a function of the subleading b jet transverse momentum for the Z + >= 2 b jet events

Differential cross section distribution as a function of the Z boson transverse momentum for the Z + >= 2 b jet events

Normalized differential cross section distribution as a function of the Z boson transverse momentum for the Z + >= 2 b jet events

Differential cross section as a function of the angular separation between two b jets for the Z + >= 2 b jet events

Normalized differential cross section as a function of the angular separtion between two b jets for the Z + >= 2 b jet events

Differential cross section as a function of the minimum angular separation between the Z boson and two b jets for the Z + >= 2 b jet events

Normalized differential cross section as a function of the minimum angular separation between the Z boson and two b jets for the Z + >= 2 b jet events

Differential cross section as a function of the asymmetry of the Z + >= 2 b jets system

Normalized differential cross section as a function of the asymmetry of the Z + >= 2 b jets system

Differential cross section as a function of the invariant mass of two b jets for the Z + >= 2 b jet events

Normalized differential cross section as a function of the invariant mass of two b jets for the Z + >= 2 b jet events

Differential cross section as a function of the invariant mass of the Z boson and two b jets for the Z + >= 2 b jet events

Normalized differential cross section as a function of invariant mass of the Z boson and two b jets for the Z + >= 2 b jet events

Distributions of the cross section ratios as a function of the leading b jet transverse momentum

Distributions of the cross section ratios as a function of the leading b jet absolute pseudorapidity