Post-fit distributions for the number of jets ($N_{j}$) in CR 1b(-). The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Post-fit distribution for the difference between the number of positive events and the number of negative events ($N_{+}-N_{-}$) as a function of the number of jets ($N_{j}$) in the sum of four $t\bar{t}W$ CRs and the SR.

Post-fit distributions for the fitted variables in the CRs for the fake/non-prompt lepton background - CR HF e. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Post-fit distributions for the fitted variables in the CRs for the fake/non-prompt lepton background - CR HF $\mu$. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Post-fit distributions for the fitted variables in the CRs for the fake/non-prompt lepton background - CR Mat. Conv.. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Post-fit distributions for the fitted variables in the CRs for the fake/non-prompt lepton background - CR Low $m_{\gamma*}$. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and the predictions after a fit to data for the GNN distribution in the SR. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and prediction after the fit to data in the signal-enriched region with GNN >= 0.6 for the distributions of the number of jets. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and prediction after the fit to data in the signal-enriched region with GNN >= 0.6 for the distributions of the number of b-jets. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and prediction after the fit to data in the signal-enriched region with GNN >= 0.6 for the distributions of the sum of the four highest PCBT scores of jets in the event. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Comparison between data and prediction after the fit to data in the signal-enriched region with GNN >= 0.6 for the distributions of the sum of transverse momenta over all jets and leptons in the event. The QmisID represents the backgrounds with a mis-assigned charge. HF e and HF $\mu$ are the backgrounds with fake/non-prompt leptons. Mat. Conv. and Low $m_{\gamma*}$ are the material and virtual photon conversions.

Two-dimensional negative log-likelihood contour for the $t\bar{t}t$ cross section ($\sigma_{t\bar{t}t}$) versus the $t\bar{t}t\bar{t}$ cross section ($\sigma_{t\bar{t}t\bar{t}}$) when the normalisation of both processes are treated as free parameters in the fit.

The negative log-likelihood values as a function of the Higgs oblique parameter $\hat{H}$.

The measured and expected cross-sections of $t\bar{t}t\bar{t}$ production.

Observed 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled. This is the lower part of the contour.

Observed 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled. This is the upper part of the contour.

Observed 95% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Expected 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Expected 95% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Observed 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled. This is the lower part of the contour.

Observed 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled. This is the upper part of the contour.

Observed 95% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Expected 68% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Expected 95% CL Limit Contour in $\kappa_t,\alpha$ with $t\bar tt\bar t$ parameterized as a function of $\kappa_t,\alpha$, and the $t\bar tH$ parameterization profiled.

Reconstruction level differential cross section spectla, obtained for the central acceptance, $|\eta| < 3$, for events with Pomeron-Proton ($\mathrm{I\!P}\mathrm{p} + \gamma \mathrm{p}$) topology compared to the to the EPOS-LHC predictions, broken down into the non-diffractive (ND), central diffractive (CD), single diffractive (SD) and double diffractive (DD) components. Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV

The number of high purity tracksin the first $\eta$ bin after a gap of $4.5 \leq \Delta\eta^F < 5.0$ for events with the Pomeron-Proton ($\mathrm{I\!P}\mathrm{p} + \gamma \mathrm{p}$) topology Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV

The transeverse momentum of high purity tracksin the first $\eta$ bin after a gap of $4.5 \leq \Delta\eta^F < 5.0$ for events with the Pomeron-Proton ($\mathrm{I\!P}\mathrm{p} + \gamma \mathrm{p}$) topology Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV

The total energy of all Particle Flow candidatesin the first $\eta$ bin after a gap of $4.5 \leq \Delta\eta^F < 5.0$ for events with the Pomeron-Proton ($\mathrm{I\!P}\mathrm{p} + \gamma \mathrm{p}$) topology Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV

Unfolded diffraction enhanced differential cross section for events with Pomeron-Lead ($\mathrm{I\!P}\mathrm{Pb}$) topology, defined on the region $|\eta| < 5.2$, compared to hadron level predictions of the MC generators. The data are corrected for the contribution from events with undetectable energy in the HF calorimeter adjacent to the rapidity gap. The corrections are obtained using the EPOS-LHC MC sample. Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV $|\eta| > 3.0$: No particles

Unfolded diffraction enhanced differential cross section for events with Pomeron-Proton ($\mathrm{I\!P}\mathrm{p} + \gamma \mathrm{p}$) topology, defined on the region $|\eta| < 5.2$, compared to hadron level predictions of the MC generators. The data are corrected for the contribution from events with undetectable energy in the HF calorimeter adjacent to the rapidity gap. The corrections are obtained using the EPOS-LHC MC sample. Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV $|\eta| > 3.0$: No particles

Unfolded diffractive enhanced differential cross section spectla for events with Pomeron-Lead ($\mathrm{I\!P}\mathrm{Pb}$) topology, compared to the to the hadron-level EPOS-LHC predictions, broken down into the non-diffractive (ND), central diffractive (CD), single diffractive (SD) and double diffractive (DD) components. Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV $|\eta| > 3.0$: No particles

Unfolded diffractive enhanced differential cross section spectla for events with Pomeron-Proton ($\mathrm{I\!P}\mathrm{p} + \gamma \mathrm{p}$) topology, compared to the to the hadron-level EPOS-LHC predictions, broken down into the non-diffractive (ND), central diffractive (CD), single diffractive (SD) and double diffractive (DD) components. Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV $|\eta| > 3.0$: No particles

Unfolded diffractive enhanced differential cross section spectla for events with Pomeron-Lead ($\mathrm{I\!P}\mathrm{Pb}$) topology, compared to the to the hadron-level QGSJET II predictions, broken down into the non-diffractive (ND), central diffractive (CD), single diffractive (SD) and double diffractive (DD) components. Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV $|\eta| > 3.0$: No particles

Unfolded diffractive enhanced differential cross section spectla for events with Pomeron-Proton ($\mathrm{I\!P}\mathrm{p} + \gamma \mathrm{p}$) topology, compared to the to the hadron-level QGSJET II predictions, broken down into the non-diffractive (ND), central diffractive (CD), single diffractive (SD) and double diffractive (DD) components. Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV $|\eta| > 3.0$: No particles

Reconstruction level diffraction enhanced differential cross section spectrum for Pomeron-Proton ($\mathrm{I\!P}\mathrm{p} + \gamma \mathrm{p}$) topology corrected for the contribution from events with undetectable energy in the HF calorimeter adjacent to the rapidity gap, compared with the spectrum obtained with events satisfying the ZDC veto requirement $E_{ZDC−} < 1 \mathrm{~TeV}$ which selects only the events without lead nuclear break up (without correction for HF undetectable energy). Forward Rapidity Gap definition: $|\eta| < 2.5$: $p_{T}^{track} < 200$ MeV and $\sum \limits_{bin} E^{PF} < 6$ GeV $|\eta| \in [2.5,3.0]$: $\sum \limits_{bin} E_{neutral}^{PF} < 13.4$ GeV $|\eta| > 3.0$: No particles

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Observed CLs contour with plus 1-sigma signal cross-section uncertainty in the ( M(CHARGINO), TAU(CHARGINO) ) space for tan(beta) = 5 and mu > 0.

Expected CLs contour in the ( M(CHARGINO), TAU(CHARGINO) ) space for tan(beta) = 5 and mu > 0.

Expected CLs contour with minus 1-sigma experimental uncertainty in the ( M(CHARGINO), TAU(CHARGINO) ) space for tan(beta) = 5 and mu > 0.

Expected CLs contour with plus 1-sigma experimental uncertainty in the ( M(CHARGINO), TAU(CHARGINO) ) space for tan(beta) = 5 and mu > 0.

Observed CLs contour in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Observed CLs contour with minus 1-sigma signal cross-section uncertainty in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Observed CLs contour with plus 1-sigma signal cross-section uncertainty in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Expected CLs contour in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Expected CLs contour with minus 1-sigma experimental uncertainty in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

Expected CLs contour with plus 1-sigma experimental uncertainty in the ( M(CHARGINO), M(CHARGINO)-M(NEUTRALINO) ) space of the AMSB model for tan(beta) = 5 and mu > 0.

The K0S cross section for 5 jet ET ranges.

The LAMBDA cross section for 5 jet ET ranges.

The corrected per-event transverse momentum distribution of Z bosons in the centrality region 10-20%.

The corrected per-event transverse momentum distribution of Z bosons in the centrality region 20-40%.

The corrected per-event transverse momentum distribution of Z bosons in the centrality region 40-80%.

Combined results for the centrality (Npart) dependence of Z boson yields divided by Ncoll for the PT range > 0 GeV/c. The systematic error includes the uncertainty in Ncoll.

Combined results for the centrality (Npart) dependence of Z boson yields divided by Ncoll for the PT range 0 to 10 GeV/c. The systematic error includes the uncertainty in Ncoll.

Combined results for the centrality (Npart) dependence of Z boson yields divided by Ncoll for the PT range 10 to 30 GeV/c. The systematic error includes the uncertainty in Ncoll.

Combined results for the centrality (Npart) dependence of Z boson yields divided by Ncoll for the PT range > 30 GeV/c. The systematic error includes the uncertainty in Ncoll.

Correlations in the PT distribution.

Normalised fiducial cross section in bins of the Mass of the WZ.

Correlations in the Mass distribution.

Acceptance in the Lambda and tan(beta) plane. Note that bins with zero content in both Lambda abd Tan(Beta) have been omitted.

Efficiency in the Lambda and tan(beta) plane. Note that bins with zero content in both Lambda abd Tan(Beta) have been omitted.

Total systematic and theoretical signal uncertainty in the Lambda and tan(beta) plane. Note that bins with zero content in both Lambda abd Tan(Beta) have been omitted.

The observed CLS values in the Lambda and tan(beta) plane. Note that bins with zero content in both Lambda abd Tan(Beta) have been omitted.

The number of generated GMSB signal events in the Lambda and tan(beta) plane. Note that bins with zero content in both Lambda abd Tan(Beta) have been omitted.

The total signal production cross section in the Lambda and tan(beta) plane in pb Note that bins with zero content in both Lambda abd Tan(Beta) have been omitted.

Expected 95% C.L. exclusion limits in the Mmess = 250 TeV, N5 = 3, mu > 0, Cgrav = 1 slice of GMSB in the Lambda and tan(beta) plane.

Observed 95% C.L. exclusion limits in the Mmess = 250 TeV, N5 = 3, mu > 0, Cgrav = 1 slice of GMSB in the Lambda and tan(beta) plane.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 0.8 to 6.8.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 6.8 to 12.8.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 12.8 to 18.8.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 18.8 to 24.8.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 24.8 to 30.8.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 30.8 to 36.8.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 36.8 to 42.8.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 42.8 to 48.8.

The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 48.8 to 51.2.

The observed and expected 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta).

The acceptance, efficiency and their product in the Lambda-Tan(Beta)-plane for M_mess=250 TeV, N_5=3 and C_grav=1.

The relative systematic uncertainty from the selection, theoretical and statistical uncertainities and their combined values.

The observed 95% CL upper limits on the visible and production cross sections in the Lambda-Tan(Beta) plane for stau and slepton NLSP production in the minimal GMSB model.