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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64822.9 $\pm$ 254.6, NPowHeg truth =634276.

Unfolded dR distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64273.2 $\pm$ 253.5, NPowHeg truth =338671.

Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 65362.4 $\pm$ 255.7 , NPowHeg truth =634214.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63486.8 $\pm$ 252.0, NPowHeg truth =333348.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64432.6 $\pm$ 253.8, NPowHeg truth =624059.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57095.9 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =76327.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57130 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =763273.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58310.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58320.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Unfolded dR distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57705.4 $\pm$ 254 , NPowHeg truth =301655, N Sherpa truth =763259.

Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58869.2 $\pm$ 256, NPowHeg truth =564660, NS herpa truth =813995.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57358.6 $\pm$ 254 , NPowHeg truth =298662, N Sherpa truth =756609.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58226.4 $\pm$ 254, NPowHeg truth =558949, N Sherpa truth =806988.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63717.4 $\pm$ 252.4, NPowHeg truth =338714.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63855.8 $\pm$ 252.7 , NPowHeg truth =338708.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64809.8 $\pm$ 254.6, NPowHeg truth =634285.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64822.9 $\pm$ 254.6, NPowHeg truth =634276.

Unfolded dR distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64273.2 $\pm$ 253.5, NPowHeg truth =338671.

Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 65362.4 $\pm$ 255.7 , NPowHeg truth =634214.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63486.8 $\pm$ 252.0, NPowHeg truth =333348.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64432.6 $\pm$ 253.8, NPowHeg truth =624059.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57095.9 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =76327.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57130 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =763273.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58310.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58320.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Unfolded dR distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57705.4 $\pm$ 254 , NPowHeg truth =301655, N Sherpa truth =763259.

Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58869.2 $\pm$ 256, NPowHeg truth =564660, NS herpa truth =813995.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57358.6 $\pm$ 254 , NPowHeg truth =298662, N Sherpa truth =756609.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58226.4 $\pm$ 254, NPowHeg truth =558949, N Sherpa truth =806988.

Truth $P_{T}^{Z}$ distribution after reweghting to data.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63717.4 $\pm$ 252.4, NPowHeg truth =338714.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63855.8 $\pm$ 252.7 , NPowHeg truth =338708.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64809.8 $\pm$ 254.6, NPowHeg truth =634285.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64822.9 $\pm$ 254.6, NPowHeg truth =634276.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64273.2 $\pm$ 253.5, NPowHeg truth =338671.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 65362.4 $\pm$ 255.7 , NPowHeg truth =634214.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63486.8 $\pm$ 252.0, NPowHeg truth =333348.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64432.6 $\pm$ 253.8, NPowHeg truth =624059.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57095.9 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =76327.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57130 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =763273.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58310.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58320.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57705.4 $\pm$ 254 , NPowHeg truth =301655, N Sherpa truth =763259.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58869.2 $\pm$ 256, NPowHeg truth =564660, NS herpa truth =813995.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57358.6 $\pm$ 254 , NPowHeg truth =298662, N Sherpa truth =756609.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58226.4 $\pm$ 254, NPowHeg truth =558949, N Sherpa truth =806988.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63717.4 $\pm$ 252.4, NPowHeg truth =338714.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63855.8 $\pm$ 252.7 , NPowHeg truth =338708.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64809.8 $\pm$ 254.6, NPowHeg truth =634285.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64822.9 $\pm$ 254.6, NPowHeg truth =634276.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64273.2 $\pm$ 253.5, NPowHeg truth =338671.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 65362.4 $\pm$ 255.7 , NPowHeg truth =634214.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63486.8 $\pm$ 252.0, NPowHeg truth =333348.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64432.6 $\pm$ 253.8, NPowHeg truth =624059.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57095.9 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =76327.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57130 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =763273.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58310.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58320.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57705.4 $\pm$ 254 , NPowHeg truth =301655, N Sherpa truth =763259.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58869.2 $\pm$ 256, NPowHeg truth =564660, NS herpa truth =813995.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57358.6 $\pm$ 254 , NPowHeg truth =298662, N Sherpa truth =756609.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58226.4 $\pm$ 254, NPowHeg truth =558949, N Sherpa truth =806988.

Unfolded $M(l^{+}\gamma\gamma)$ distribution for $Z \to ll\gamma\gamma$ process with dressed leptons.

Unfolded $M(l^{+}\gamma\gamma)$ distribution for $Z \to ll\gamma\gamma$ process with bare leptons.

Unfolded dR distribution for $Z \to ll\gamma\gamma$ process with dressed leptons for leading photon.

Unfolded dR distribution for $Z \to ll\gamma\gamma$ process with bare leptons for leading photon.

Unfolded dR distribution for $Z \to ll\gamma\gamma$ process with dressed leptons for second photon.

Unfolded dR distribution for $Z \to ll\gamma\gamma$ process with bare leptons for second photon.

Unfolded dRgg distribution for $Z \to ll\gamma\gamma$ process with dressed leptons.

Unfolded dRgg distribution for $Z \to ll\gamma\gamma$ process with bare leptons.

Unfolded $P_{T}^{\gamma 1}$ distribution for $Z \to ll\gamma\gamma$ process with dressed leptons for leading photon.

Unfolded $P_{T}^{\gamma 1}$ distribution for $Z \to ll\gamma\gamma$ process with bare leptons for leading photon.

Unfolded $P_{T}^{\gamma 2}$ distribution for $Z \to ll\gamma\gamma$ process with dressed leptons for second photon.

Unfolded $P_{T}^{\gamma 2}$ distribution for $Z \to ll\gamma\gamma$ process with bare leptons for second photon.

Expected 2-D likelihood scan of the signal strength $\mu_{\tau\tau}$ versus the $CP$-mixing angle $\phi_{\tau}$.

Free-floating parameters in the measurement.

Naming convention for the observables at different levels of the analysis. At the background-subtracted level the contributions of tracks from pile-up collisions and tracks from secondary vertices are subtracted. At the corrected level the tracking-efficiency correction (TEC) is applied. The observables at particle level are the analysis results.

The total pile-up scale-factor relative uncertainty parameterised in $\mu$ and $n_\text{trk,out}$ and expressed in percent.

The $\chi^2$ and NDF for measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. Global($n_\text{ch},\Sigma_{n_{\text{ch}}} p_{\text{T}}$) denotes the scenario in which the covariance matrix is built including the correlations of systematic uncertainties between the two observables $n_{\text{ch}}$ and $\Sigma_{n_{\text{ch}}} p_{\text{T}}$

Normalised differential cross-section as a function of $n_\text{ch}$.

Normalised differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n\text{ch}$ in $ n_\text{ch} \geq 80$.

The $\chi^2$ and NDF for the measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. The values corresponding to the `Total' scenario are given. In this scenario, only uncertainties described in Section 8 of the paper are included, while the extra theory uncertainties defined in Section 9 are omitted.

The $\chi^2$ and NDF for the measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. The values corresponding to the `De-correlate modelling' scenario are given. This scenario uses the detector covariance matrix, and adding modelling uncertainties, scale variations in the matrix element and parton shower as well as the $h_{\text{damp}}$ variation only to the diagonal elements of the covariance matrix.

The $\chi^2$ and NDF for measured absolute differential cross-sections obtained by comparing the different predictions with the unfolded data. Global($n_\text{ch},\Sigma_{n_{\text{ch}}} p_{\text{T}}$) denotes the scenario in which the covariance matrix is built including the correlations of systematic uncertainties between the two observables $n_{\text{ch}}$ and $\Sigma_{n_{\text{ch}}} p_{\text{T}}$

The $\chi^2$ and NDF for the measured absolute differential cross-sections obtained by comparing the different predictions with the unfolded data. The values corresponding to the `Total' scenario are given. In this scenario, only uncertainties described in Section 8 of the paper are included, while the extra theory uncertainties defined in Section 9 are omitted.

The $\chi^2$ and NDF for the measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. The values corresponding to the `De-correlate modelling' scenario are given. This scenario uses the detector covariance matrix, and adding modelling uncertainties, scale variations in the matrix element and parton shower as well as the $h_{\text{damp}}$ variation only to the diagonal elements of the covariance matrix.

Absolute differential cross-section as a function of $n_\text{ch}$.

Absolute differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n\text{ch}$ in $ n_\text{ch} \geq 80$.

Covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix of the normalised differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ vs. $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix of the normalised differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ vs. $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix of the absolute differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ vs. $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix of the absolute differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ vs. $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Distribution of $m(e^{\pm},\mu^{\pm})_{\mathrm{lead}}$ in the electron-muon signal region after the background-only fit.

Distribution of $m(\mu^{\pm},\mu^{\pm})_{\mathrm{lead}}$ in the muon-muon signal region after the background-only fit.

Distribution of $m(\ell^{\pm},\ell^{\prime \pm})_{\mathrm{lead}}$ in the three-lepton signal region after the background-only fit.

Event yield in the four-lepton signal region after the background-only fit.

Two-lepton signal region weighted cutflows for electron-electron channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark and multiboson process backgrounds are merged under the 'other' category.

Two-lepton signal region weighted cutflows for electron-muon channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark and multiboson process backgrounds are merged under the 'other' category.

Two-lepton signal region weighted cutflows for muon-muon channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark and multiboson process backgrounds are merged under the 'other' category.

Three-lepton signal region weighted cutflow for $\ell \ell \ell$ channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark and multiboson process backgrounds are merged under the 'other' category.

Four-lepton signal region weighted cutflow for $\ell \ell \ell \ell$ channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark, Drell-Yan, and multiboson process backgrounds are merged under the 'other' category.

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a W boson decaying to a charged lepton and a neutrino.

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a Z boson decaying to charged leptons.

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a Z boson decaying to charged leptons.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Expected, with the corresponding $\pm 1\sigma$ intervals, and observed 95% CL branching fraction upper limits for the Higgs and $Z$ boson decays into a quarkonium state and a photon. Standard Model production of the Higgs boson is assumed. The corresponding upper limits on the production cross section times branching fraction $\sigma\times\mathcal{B}$ are also shown.

Best fit values of $\sigma(gg\to H\to ZZ)$, $\sigma_i/\sigma_{gg\mathrm{F}}$, and $\mathrm{B}^f/\mathrm{B}^{ZZ}$ relative to their SM prediction from the combined analysis of the $\sqrt{s}$=7 and 8 TeV data. The results are shown for the combination of ATLAS and CMS, and also separately for each experiment, together with their total uncertainties and their breakdown into the four components described in the text. The expected uncertainties in the measurements are also shown.

Best fit values of $\sigma(gg\to H\to WW)$, $\sigma_i/\sigma_{gg\mathrm{F}}$, and $\mathrm{B}^f/\mathrm{B}^{WW}$ from the combined analysis of the $\sqrt{s}$=7 and 8 TeV data. The values involving cross sections are given for $\sqrt{s}$=8 TeV, assuming the SM values for $\sigma_i(7 \mathrm{TeV})/\sigma_i(8 \mathrm{TeV})$. The results are shown for the combination of ATLAS and CMS, and also separately for each experiment, together with their total uncertainties and their breakdown into the four components described in the text. The expected uncertainties in the measurements are also shown.

Best fit values of $\sigma(gg\to H\to WW)$, $\sigma_i/\sigma_{gg\mathrm{F}}$, and $\mathrm{B}^f/\mathrm{B}^{WW}$ relative to their SM prediction from the combined analysis of the $\sqrt{s}$=7 and 8 TeV data. The results are shown for the combination of ATLAS and CMS, and also separately for each experiment, together with their total uncertainties and their breakdown into the four components described in the text. The expected uncertainties in the measurements are also shown.

Best fit values of $\kappa_{gZ}= \kappa_{g}\cdot\kappa_{Z} / \kappa_{H}$ and of the ratios of coupling modifiers, as defined in the most generic parameterisation described in the context of the $\kappa$ framework, from the combined analysis of the $\sqrt{s}$=7 and 8 TeV data. The results are shown for the combination of ATLAS and CMS and also separately for each experiment, together with their total uncertainties and their breakdown into the four components described in the text. The uncertainties in $\lambda_{tg}$ and $\lambda_{WZ}$, for which a negative solution is allowed, are calculated around the overall best fit value. The expected total uncertainties in the measurements are also shown. For those parameters with no sensitivity to the sign, only the absolute values are shown.

Measured global signal strength $\mu$ and its total uncertainty, together with the breakdown of the uncertainty into its four components as defined in the text. The results are shown for the combination of ATLAS and CMS, and separately for each experiment. The expected uncertainty, with its breakdown, is also shown.

Measured signal strengths $\mu$ and their total uncertainties for different Higgs boson production processes. The results are shown for the combination of ATLAS and CMS, and separately for each experiment, for the combined $\sqrt{s}$=7 and 8 TeV data. The expected uncertainties in the measurements are also displayed. These results are obtained assuming that the Higgs boson branching fractions are the same as in the SM.

Measured signal strengths $\mu$ and their total uncertainties for different Higgs boson decay channels. The results are shown for the combination of ATLAS and CMS, and separately for each experiment, for the combined $\sqrt{s}$=7 and 8 TeV data. The expected uncertainties in the measurements are also displayed. These results are obtained assuming that the Higgs boson production process cross sections at $\sqrt{s}$ = 7 and 8 TeV are the same as in the SM.

Results of the ten-parameter fit of $\mu_F^f = \mu_{gg\mathrm{F}+ttH}^f$ and $\mu_V^f = \mu_{\mathrm{VBF}+VH}^f$ for each of the five decay channels. The results are shown for the combination of ATLAS and CMS, together with their measured and expected uncertainties. The measured results are also shown separately for each experiment.

Results of the six-parameter fit of the global ratio $\mu_V/\mu_F = \mu_{\mathrm{VBF}+VH}/\mu_{gg\mathrm{F}+ttH}$ together with $\mu_F^f$ for each of the five decay channels. The results are shown for the combination of ATLAS and CMS, together with their measured and expected uncertainties. The measured results are also shown separately for each experiment.

Fit results allowing BSM loop couplings, assuming that $|\kappa_{V}| \le$ 1, where $\kappa_{V}$ denotes $\kappa_{Z}$ or $\kappa_{W}$, and that $\mathrm{B_{BSM}} \ge$ 0. The results for the combination of ATLAS and CMS are reported with their measured and expected uncertainties. Also shown are the results from each experiment. For the parameters with both signs allowed, the other 1$\sigma$ interval is shown on a second line. For those parameters with no sensitivity to the sign, only the absolute values are shown.

Fit results allowing BSM loop couplings, assuming that there are no additional BSM contributions to the Higgs boson width, i.e. $\mathrm{B_{BSM}}=0$. The results for the combination of ATLAS and CMS are reported with their measured and expected uncertainties. Also shown are the results from each experiment. For the parameters with both signs allowed, the other 1$\sigma$ interval is shown on a second line. When a parameter is constrained and reaches a boundary, namely $\mathrm{B_{BSM}}$ = 0, the uncertainty is not indicated. For those parameters with no sensitivity to the sign, only the absolute values are shown.

Fit results for the parameterisation assuming the absence of BSM particles in the loops ($\mathrm{B_{BSM}}$=0). The results with their measured and expected uncertainties are reported for the combination of ATLAS and CMS, together with the individual results from each experiment. For the parameters with both signs allowed, the other 1$\sigma$ CL interval is shown on a second line. When a parameter is constrained and reaches a boundary, namely $|\kappa_\mu|$ = 0, the uncertainty is not indicated. For those parameters with no sensitivity to the sign, only the absolute values are shown.

Summary of fit results for a parameterisation probing the ratios of coupling modifiers for up-type versus down-type fermions. The results for the combination of ATLAS and CMS are reported together with their measured and expected uncertainties. Also shown are the results from each experiment. The parameter $\kappa_{uu}$ is positive definite since $\kappa_H$ is always assumed to be positive. For the parameter $\lambda_{du}$, for which both signs are allowed, the other 1$\sigma$ CL interval is shown on a second line. Negative values for the parameter $\lambda_{Vu}$ are excluded by more than 4$\sigma$.

Summary of fit results for a parameterisation probing the ratios of coupling modifiers for leptons versus quarks. The results for the combination of ATLAS and CMS are reported together with their measured and expected uncertainties. Also shown are the results from each experiment. The parameter $\kappa_{qq}$ is positive definite since $\kappa_H$ is always assumed to be positive. For the parameter $\lambda_{lq}$, for which there is no sensitivity to the sign, only the absolute values are shown. Negative values for the parameter $\lambda_{Vq}$ are excluded by more than 4$\sigma$.

Correlation matrix obtained from the fit combining the ATLAS and CMS data using the generic parameterisation with 23 parameters, $\sigma_i\cdot\mathrm{B}^f$ for each specific channel $i\to H\to f$. Only 20 parameters are shown because the other three, corresponding to the $H\to ZZ$ decay channel for the $WH$, $ZH$, and $ttH$ production processes, are not measured with a meaningful precision.

Correlation matrix obtained from the fit combining the ATLAS and CMS data using the generic parameterisation with nine parameters, $\sigma(gg\to H\to ZZ)$, $\sigma_i/\sigma_{gg\mathrm{F}}$, and $\mathrm{B}^f/\mathrm{B}^{ZZ}$.

Correlation matrix obtained from the fit combining the ATLAS and CMS data using the generic parameterisation with seven parameters, $\kappa_{gZ}=\kappa_{g}\cdot\kappa_{Z} / \kappa_{H}$ and the ratios of coupling modifiers.

Expected correlation matrix obtained from the fit combining ATLAS and CMS pre-fit Asimov data sets using the generic parameterisation with 23 parameters, $\sigma_i\cdot\mathrm{B}^f$ for each specific channel $i\to H\to f$. Only 20 parameters are shown because the other three, corresponding to the $H\to ZZ$ decay channel for the $WH$, $ZH$, and $ttH$ production processes, are not measured with a meaningful precision.

Expected correlation matrix obtained from the fit combining ATLAS and CMS pre-fit Asimov data sets using the generic parameterisation with nine parameters, $\sigma(gg\to H\to ZZ)$, $\sigma_i/\sigma_{gg\mathrm{F}}$, and $\mathrm{B}^f/\mathrm{B}^{ZZ}$.

Expected correlation matrix obtained from the fit combining ATLAS and CMS pre-fit Asimov data sets using the generic parameterisation with seven parameters, $\kappa_{gZ}=\kappa_{g}\cdot\kappa_{Z} / \kappa_{H}$ and the ratios of coupling modifiers.

ATLAS+CMS combined best-fit and 68% and 95% CL negative log-likelihood contours in the ($\kappa_{g}$,$\kappa_{\gamma}$) plane.

ATLAS best-fit and 68% and 95% CL negative log-likelihood contours in the ($\kappa_{g}$,$\kappa_{\gamma}$) plane.

CMS best-fit and 68% and 95% CL negative log-likelihood contours in the ($\kappa_{g}$,$\kappa_{\gamma}$) plane.

Best-fit and 68% and 95% CL negative log-likelihood contours in the ($\kappa_{F}$,$\kappa_{V}$) plane.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow\gamma\gamma$ channel.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow ZZ$ channel.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow WW$ channel.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow\tau\tau$ channel.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow bb$ channel.

ATLAS best-fit and 68% and 95% CL negative log-likelihood contours in the ($\kappa_{F}$,$\kappa_{V}$) plane.

CMS best-fit and 68% and 95% CL negative log-likelihood contours in the ($\kappa_{F}$,$\kappa_{V}$) plane.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow\gamma\gamma$ channel, assuming $\kappa_{F}>0$.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow ZZ$ channel, assuming $\kappa_{F}>0$.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow WW$ channel, assuming $\kappa_{F}>0$.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow\tau\tau$ channel, assuming $\kappa_{F}>0$.

Best-fit and 68% CL negative log-likelihood contour in the ($\kappa_{F}$,$\kappa_{V}$) plane for the $H\rightarrow bb$ channel, assuming $\kappa_{F}>0$.

Best-fit and 68% and 95% CL negative log-likelihood contours in the ($\mu^{f}_{gg\mathrm{F}+ttH}$,$\mu^{f}_{\mathrm{VBF}+VH}$) plane for the $H\rightarrow\gamma\gamma$ channel.

Best-fit and 68% and 95% CL negative log-likelihood contours in the ($\mu^{f}_{gg\mathrm{F}+ttH}$,$\mu^{f}_{\mathrm{VBF}+VH}$) plane for the $H\rightarrow ZZ$ channel.

Best-fit and 68% and 95% CL negative log-likelihood contours in the ($\mu^{f}_{gg\mathrm{F}+ttH}$,$\mu^{f}_{\mathrm{VBF}+VH}$) plane for the $H\rightarrow WW$ channel.

Best-fit and 68% and 95% CL negative log-likelihood contours in the ($\mu^{f}_{gg\mathrm{F}+ttH}$,$\mu^{f}_{\mathrm{VBF}+VH}$) plane for the $H\rightarrow\tau\tau$ channel.

Best-fit and 68% and 95% CL negative log-likelihood contours in the ($\mu^{f}_{gg\mathrm{F}+ttH}$,$\mu^{f}_{\mathrm{VBF}+VH}$) plane for the $H\rightarrow bb$ channel.