Distribution of an input to the BDT classifier in the $3\ell$ category: The linear discrimminant $p_{T}^{miss,LD}$. The discriminant is defined by the relation $p_{T}^{miss,LD} = 0.6 p_{T}^{miss} + 0.4 H_{T}^{miss}$, where $H_{T}^{miss}$ corresponds to the magnitude of the vector $p_{T}$ sum of all electrons, muons, taus and small radius jets passing the selection critertia, and $p_{T}^{miss}$ represents the missing transverse momentum vector computed as the negative vector $p_{T}$ sum of all the particles reconstructed by the PF algorithm in the event.

Distributions of the transverse mass $m_{T}$ of the lepton that is not originating from the Z boson decay and the missing transverse momentum in the $3\ell$/WZ control region. The distributions expected for the WZ and ZZ as well as for other background processes are shown for the values of nuisance parameters obtained from the ML fit used in the signal extraction.

Distributions of the four lepton invariant mass $m_{4\ell}$ in the $4\ell$/ZZ control region. The distributions expected for the ZZ as well as for other background processes are shown for the values of nuisance parameters obtained from the ML fit used in the signal extraction.

Distributions of the transverse mass $m_{T}$ of the leading lepton and the missing transverse momentum in the $2\ell$(ss) control region. The distributions expected for the misidentified $\ell$ background as well as for other background processes is shown for the values of nuisance parameters obtained from the background-only ML fit, in which the HH signal is constrained to be zero.

Distributions of the reconstructed HH mass $m_{HH}$ in the $2\ell+2 au_{h}$ control region. The distributions expected for the misidentified $\ell/ au_{h}$ background as well as for other background processes is shown for the values of nuisance parameters obtained from the background-only ML fit, in which the HH signal is constrained to be zero.

Distribution in the output of the BDT trained for nonresonant HH production and evaluated for the benchmark scenario JHEP04 BM7 for the $2\ell$(ss) category. The SM HH signal is shown for a cross section amounting to $30$ times the value predicted in the SM. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate signal quantiles.

Distribution in the output of the BDT trained for nonresonant HH production and evaluated for the benchmark scenario JHEP04 BM7 for the $3\ell$ category. The SM HH signal is shown for a cross section amounting to $30$ times the value predicted in the SM. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate signal quantiles.

Distribution in the output of the BDT trained for nonresonant HH production and evaluated for the benchmark scenario JHEP04 BM7 for the $4\ell$ category. The SM HH signal is shown for a cross section amounting to $30$ times the value predicted in the SM. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate signal quantiles.

Distribution in the output of the BDT trained for nonresonant HH production and evaluated for the benchmark scenario JHEP04 BM7 for the $3\ell+1 au_{h}$ category. The SM HH signal is shown for a cross section amounting to $30$ times the value predicted in the SM. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate background quantiles.

Distribution in the output of the BDT trained for nonresonant HH production and evaluated for the benchmark scenario JHEP04 BM7 for the $2\ell+2 au_{h}$ category. The SM HH signal is shown for a cross section amounting to $30$ times the value predicted in the SM. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate background quantiles.

Distribution in the output of the BDT trained for nonresonant HH production and evaluated for the benchmark scenario JHEP04 BM7 for the $1\ell+3 au_{h}$ category. The SM HH signal is shown for a cross section amounting to $30$ times the value predicted in the SM. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate background quantiles.

Distribution in the output of the BDT trained for nonresonant HH production and evaluated for the benchmark scenario JHEP04 BM7 for the $4 au_{h}$ category. The SM HH signal is shown for a cross section amounting to $30$ times the value predicted in the SM. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate background quantiles.

Observed and expected $95\%$ CL upper limits on the SM HH production cross section, obtained for both individual search categories and from a simultaneous fit of all seven categories combined.

Observed and expected $95\%$ CL upper limits on the HH production cross section as a function of the Higgs boson self-coupling strength modifier $\kappa_\lambda$ for the combination of all seven categories. All Higgs boson couplings other than $\lambda$ are assumed to have the values predicted in the SM.

Observed and expected $95\%$ CL upper limits on the HH production cross section as a function of the Higgs boson self-coupling strength modifier $\kappa_\lambda$ for the seven different categories as well as their combination. All Higgs boson couplings other than $\lambda$ are assumed to have the values predicted in the SM.

Observed and expected $95\%$ CL upper limits on the HH production cross section for the twelve benchmark scenarios from doi:10.1007/JHEP04(2016)126, the additional benchmark scenario 8a from doi:10.1007/JHEP09(2018)057, the seven benchmark scenarios from doi:10.1007/JHEP03(2020)091, and for the SM.

Observed and expected $95\%$ CL upper limits on the HH production cross section for the twelve benchmark scenarios from doi:10.1007/JHEP04(2016)126, the additional benchmark scenario 8a from doi:10.1007/JHEP09(2018)057, the seven benchmark scenarios from doi:10.1007/JHEP03(2020)091, and for the SM. The limits are shown for all seven search categories as well as for their combination.

Observed and expected $95\%$ CL upper limits on the HH production cross section as a function of the effective coupling $c_{2}$ for the combination of all seven categories. All Higgs boson couplings other than $c_{2}$ are assumed to have the values predicted in the SM.

Observed and expected $95\%$ CL upper limits on the HH production cross section as a function of the effective coupling $c_{2}$ and the top Yukawa coupling modifier $\kappa_{t}$ for the combination of all seven categories. All Higgs boson couplings other than $c_{2}$ and $\kappa_{t}$ are assumed to have the values predicted in the SM. The position of the SM in the ($c_{2}-\kappa_{t}$) plane, as well as the best fit value of $(c_{2},\kappa_{t})=(1.05, 1.74)$ together with contours for the theory cross section are shown as well.

Observed and expected $95\%$ CL upper limits on the HH production cross section as a function of the Higgs boson self-coupling modifier $\kappa_{\lambda}$ and the top Yukawa coupling modifier $\kappa_{t}$ for the combination of all seven categories. All Higgs boson couplings other than $\kappa_{\lambda}$ and $\kappa_{t}$ are assumed to have the values predicted in the SM. The position of the SM in the ($\kappa_{\lambda}-\kappa_{t}$) plane, as well as the best fit value of $(\kappa_{\lambda},\kappa_{t})=(-3.54, -1.70)$ together with contours for the theory cross section are shown as well.

Observed and expected $95\%$ CL upper limits on the HH production cross section as a function of the effective coupling $c_{2}$ and the Higgs boson self-coupling modifier $\kappa_{\lambda}$ for the combination of all seven categories. All Higgs boson couplings other than $c_{2}$ and $\kappa_{\lambda}$ are assumed to have the values predicted in the SM. The position of the SM in the ($c_{2}-\kappa_{\lambda}$) plane, as well as the best fit value of $(c_{2},\kappa_{\lambda})=(-0.66, 5.33)$ together with contours for the theory cross section are shown as well.

Observed and expected $95\%$ CL upper limits on the production of new particles X of spin $0$ and mass $m_{X}$ in the range $250$-$1000$ GeV, which decay to Higgs boson pairs.

Observed and expected $95\%$ CL upper limits on the production of new particles X of spin $0$ and mass $m_{X}$ in the range $250$-$1000$ GeV, which decay to Higgs boson pairs. The Limit is shown for the seven different search categories as well as their combination.

Observed and expected $95\%$ CL upper limits on the production of new particles X of spin $2$ and mass $m_{X}$ in the range $250$-$1000$ GeV, which decay to Higgs boson pairs.

Observed and expected $95\%$ CL upper limits on the production of new particles X of spin $2$ and mass $m_{X}$ in the range $250$-$1000$ GeV, which decay to Higgs boson pairs. The Limit is shown for the seven different search categories as well as their combination.

Distribution in the output of the BDT trained for resonances of spin 2 and mass $750\,$GeV production for the $2\ell$(ss) category. The resonant HH signal is shown for a cross section amounting to $1\,$pb. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate signal quantiles.

Distribution in the output of the BDT trained for resonances of spin 2 and mass $750\,$GeV production for the $3\ell$ category. The resonant HH signal is shown for a cross section amounting to $1\,$pb. The distributions expected for the background processes are shown for the values of nuisance parameters obtained from the ML fit of the signal+background hypothesis to the data. The binning is chosen to approximate signal quantiles.

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.

Measured inclusive fiducial $tt\gamma$ production cross section in the dilepton final state for the different dilepton-flavour channels and combined.

Absolute differential $tt\gamma$ production cross section as a function of $p_{T}(\gamma)$ . The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $|\eta |(\gamma)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of min $\Delta R(\gamma, \ell)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $\Delta R(\gamma, \ell_{1})$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $\Delta R(\gamma, \ell_{2})$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of min $\Delta R(\gamma, b)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $|\Delta\eta(\ell\ell)|$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $\Delta \phi(\ell\ell)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $p_{T}(\ell\ell) $. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $p_{T}(\ell_{1})+p_{T}(\ell_{2})$ . The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of min $\Delta R(\ell, j)$. The values provided in the table are not divided by the bin width.

Absolute differential $tt\gamma$ production cross section as a function of $p_{T}(j_{1})$ .

Normalized differential $tt\gamma$ production cross section as a function of $p_{T}(\gamma)$ . The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $|\eta |(\gamma)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of min $\Delta R(\gamma, \ell)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $\Delta R(\gamma, \ell_{1})$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $\Delta R(\gamma, \ell_{2})$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of min $\Delta R(\gamma, b)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $|\Delta\eta(\ell\ell)|$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $\Delta \phi(\ell\ell)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $p_{T}(\ell\ell) $. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $p_{T}(\ell_{1})+p_{T}(\ell_{2})$ . The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of min $\Delta R(\ell, j)$. The values provided in the table are not divided by the bin width.

Normalized differential $tt\gamma$ production cross section as a function of $p_{T}(j_{1})$ . The values provided in the table are not divided by the bin width.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $p_{T}(\gamma)$ .

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $p_{T}(\gamma)$ .

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $|\eta |(\gamma)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $|\eta |(\gamma)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of min $\Delta R(\gamma, \ell)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of min $\Delta R(\gamma, \ell)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $\Delta R(\gamma, \ell_{1})$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $\Delta R(\gamma, \ell_{1})$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $\Delta R(\gamma, \ell_{2})$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $\Delta R(\gamma, \ell_{2})$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of min $\Delta R(\gamma, b)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of min $\Delta R(\gamma, b)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $|\Delta\eta(\ell\ell)|$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $|\Delta\eta(\ell\ell)|$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $\Delta \phi(\ell\ell)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $\Delta \phi(\ell\ell)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $p_{T}(\ell\ell) $.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $p_{T}(\ell\ell) $.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $p_{T}(\ell_{1})+p_{T}(\ell_{2})$ .

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $p_{T}(\ell_{1})+p_{T}(\ell_{2})$ .

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of min $\Delta R(\ell, j)$.

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of min $\Delta R(\ell, j)$.

Correlation matrix of the systematic uncertainty in the absolute differential cross section as a function of $p_{T}(j_{1})$ .

Correlation matrix of the statistical uncertainty in the absolute differential cross section as a function of $p_{T}(j_{1})$ .

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c_{tZ}$, using the photon pT distribution from the dilepton analysis. The value of $c^{I}_{tZ}$ is fixed to zero in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c_{tZ}$, using the combination of photon pT distributions from the dilepton and lepton+jets analyses. The value of $c^{I}_{tZ}$ is fixed to zero in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c^{I}_{tZ}$, using the photon pT distribution from the dilepton analysis. The value of $c_{tZ}$ is fixed to zero in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c^{I}_{tZ}$, using the combination of photon pT distributions from the dilepton and lepton+jets analyses. The value of $c_{tZ}$ is fixed to zero in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c_{tZ}$, using the photon pT distribution from the dilepton analysis. The value of $c^{I}_{tZ}$ is profiled in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c_{tZ}$, using the combination of photon pT distributions from the dilepton and lepton+jets analyses. The value of $c^{I}_{tZ}$ is profiled in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c^{I}_{tZ}$, using the photon pT distribution from the dilepton analysis. The value of $c_{tZ}$ is profiled in the fit.

Negative log-likelihood difference from the best-fit value for the one-dimensional scans of the Wilson coefficient $c^{I}_{tZ}$, using the combination of photon pT distributions from the dilepton and lepton+jets analyses. The value of $c_{tZ}$ is profiled in the fit.

Negative log-likelihood difference from the best-fit value as a function of Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$ from the interpretation of the dilepton measurement.

Negative log-likelihood difference from the best-fit value as a function of Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$ from the interpretation of the dilepton and lepton+jets measurements combined.

One-dimensional 68 and 95% CL intervals obtained for the Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$, using the photon $p_{T}$ distribution from the dilepton analysis, or the combination of photon pT distributions from the dilepton and lepton+jets analyses.

Comparison of observed $95\%$ CL intervals for the Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$. Results are shown from a CMS ttZ measurement [JHEP 03 (2020) 056], from a CMS ttZ & tZq interpretation [arXiv:2107.13896], from a CMS ttG (lepton+jets) measurement [arXiv:2107.01508], from this measurement, and from a global fit by J. Ellis et al. [JHEP 04 (2021) 279].

Expected $+ 1$ s.d. lower limit contour on the $m_{\mathrm{W}_{\mathrm{KK}}}$ and $m_{\mathrm{R}}$ plane after combinign with an analysis of the all-hadronic final state.

Expected - 1 s.d. lower limit contour on the $m_{\mathrm{W}_{\mathrm{KK}}}$ and $m_{\mathrm{R}}$ plane after combinign with an analysis of the all-hadronic final state.

Observed lower limit contour on the $m_{\mathrm{W}_{\mathrm{KK}}}$ and $m_{\mathrm{R}}$ plane after combinign with an analysis of the all-hadronic final state.

Post-fit distributions of the reconstructed $\ell\nu$+jets system ($m_{\mathrm{j}\ell\nu}$, $m_{\mathrm{jj}\ell\nu}$) in data and simulation for SR1.

Post-fit distributions of the reconstructed $\ell\nu$+jets system ($m_{\mathrm{j}\ell\nu}$, $m_{\mathrm{jj}\ell\nu}$) in data and simulation for SR2.

Post-fit distributions of the reconstructed $\ell\nu$+jets system ($m_{\mathrm{j}\ell\nu}$, $m_{\mathrm{jj}\ell\nu}$) in data and simulation for SR3.

Post-fit distributions of the reconstructed $\ell\nu$+jets system ($m_{\mathrm{j}\ell\nu}$, $m_{\mathrm{jj}\ell\nu}$) in data and simulation for SR5.

Post-fit distributions of the reconstructed $\ell\nu$+jets system ($m_{\mathrm{j}\ell\nu}$, $m_{\mathrm{jj}\ell\nu}$) in data and simulation for SR6.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Distribution of $m_{T}(W)$ in the $N_{jet}\geq 3$ signal region.

Distribution of $M_{3}$ in the $N_{jet}\geq 3$ signal region.

Distribution of $M_{3}$ in the $N_{jet}\geq 3$ signal region.

Distribution of $m(l,\gamma)$ in the $N_{jet}\geq 3$ signal region.

Distribution of $m(l,\gamma)$ in the $N_{jet}\geq 3$ signal region.

Distribution of $\Delta R(l,\gamma)$ in the $N_{jet}\geq 3$ signal region.

Distribution of $\Delta R(l,\gamma)$ in the $N_{jet}\geq 3$ signal region.

Distribution of $\Delta R(j,\gamma)$ in the $N_{jet}\geq 3$ signal region.

Distribution of $\Delta R(j,\gamma)$ in the $N_{jet}\geq 3$ signal region.

Fit result of the multijet template obtained with loosely isolated leptons and the electroweak background to the measured $m_{T}(W)$ distribution with isolated leptons in the $N_{jet}=2$, $N_{b jet}=0$ selection for electrons.

Fit result of the multijet template obtained with loosely isolated leptons and the electroweak background to the measured $m_{T}(W)$ distribution with isolated leptons in the $N_{jet}=2$, $N_{b jet}=0$ selection for electrons.

Fit result of the multijet template obtained with loosely isolated leptons and the electroweak background to the measured $m_{T}(W)$ distribution with isolated leptons in the $N_{jet}=2$, $N_{b jet}=0$ selection for muons.

Fit result of the multijet template obtained with loosely isolated leptons and the electroweak background to the measured $m_{T}(W)$ distribution with isolated leptons in the $N_{jet}=2$, $N_{b jet}=0$ selection for muons.

Distribution of the invariant mass of the lepton and the photon ($m(l,\gamma)$) in the $N_{jet}\geq 3$, $N_{b jet}=0$ selection for the e channel.

Distribution of the invariant mass of the lepton and the photon ($m(l,\gamma)$) in the $N_{jet}\geq 3$, $N_{b jet}=0$ selection for the e channel.

Distribution of the invariant mass of the lepton and the photon ($m(l,\gamma)$) in the $N_{jet}\geq 3$, $N_{b jet}=0$ selection for the $\mu$ channel.

Distribution of the invariant mass of the lepton and the photon ($m(l,\gamma)$) in the $N_{jet}\geq 3$, $N_{b jet}=0$ selection for the $\mu$ channel.

Extracted scale factors for the contribution from misidentified electrons for the three data-taking periods, and the Z$\gamma$, W$\gamma$ simulations.

Extracted scale factors for the contribution from misidentified electrons for the three data-taking periods, and the Z$\gamma$, W$\gamma$ simulations.

Predicted and observed yields in the control regions in the $N_{jet}= 3$ and $\geq 4$ seletions using the post-fit values of the nuisance parameters.

Predicted and observed yields in the control regions in the $N_{jet}= 3$ and $\geq 4$ seletions using the post-fit values of the nuisance parameters.

Predicted and observed yields in the signal regions in the $N_{jet}= 3$ and $\geq 4$ seletions using the post-fit values of the nuisance parameters.

Predicted and observed yields in the signal regions in the $N_{jet}= 3$ and $\geq 4$ seletions using the post-fit values of the nuisance parameters.

The measured inclusive ttgamma cross section in the fiducial phase space compared to the prediction from simulation using Madgraph_aMC@NLO at a center-of-mass energy of 13 TeV.

The measured inclusive ttgamma cross section in the fiducial phase space compared to the prediction from simulation using Madgraph_aMC@NLO at a center-of-mass energy of 13 TeV.

Summary of the measured cross section ratios with respect to the NLO cross section prediction for signal regions binned in the electron channel, muon channel and the combined single lepton measurement.

Summary of the measured cross section ratios with respect to the NLO cross section prediction for signal regions binned in the electron channel, muon channel and the combined single lepton measurement.

The unfolded differential cross sections for $p_{T}(\gamma)$ and the comparison to simulations.

The unfolded differential cross sections for $p_{T}(\gamma)$ and the comparison to simulations.

The unfolded differential cross sections for $|\eta(\gamma)|$ and the comparison to simulations.

The unfolded differential cross sections for $|\eta(\gamma)|$ and the comparison to simulations.

The unfolded differential cross sections for $\Delta R(l,\gamma)$ and the comparison to simulations.

The unfolded differential cross sections for $\Delta R(l,\gamma)$ and the comparison to simulations.

The covariance matrix of systematic uncertainties for the unfolded differential measurement for $p_{T}(\gamma)$.

The covariance matrix of systematic uncertainties for the unfolded differential measurement for $p_{T}(\gamma)$.

The covariance matrix of systematic uncertainties for the unfolded differential measurement for $|\eta(\gamma)|$.

The covariance matrix of systematic uncertainties for the unfolded differential measurement for $|\eta(\gamma)|$.

The covariance matrix of systematic uncertainties for the unfolded differential measurement for $\Delta R(l,\gamma)$.

The covariance matrix of systematic uncertainties for the unfolded differential measurement for $\Delta R(l,\gamma)$.

The covariance matrix of statistic uncertainties for the unfolded differential measurement for $p_{T}(\gamma)$.

The covariance matrix of statistic uncertainties for the unfolded differential measurement for $p_{T}(\gamma)$.

The covariance matrix of statistic uncertainties for the unfolded differential measurement for $|\eta(\gamma)|$.

The covariance matrix of statistic uncertainties for the unfolded differential measurement for $|\eta(\gamma)|$.

The covariance matrix of statistic uncertainties for the unfolded differential measurement for $\Delta R(l,\gamma)$.

The covariance matrix of statistic uncertainties for the unfolded differential measurement for $\Delta R(l,\gamma)$.

The correlation matrix of statistical uncertainties for the unfolded differential measurement for $p_{T}(\gamma)$.

The correlation matrix of statistical uncertainties for the unfolded differential measurement for $p_{T}(\gamma)$.

The correlation matrix of statistical uncertainties for the unfolded differential measurement for $|\eta(\gamma)|$.

The correlation matrix of statistical uncertainties for the unfolded differential measurement for $|\eta(\gamma)|$.

The correlation matrix of statistical uncertainties for the unfolded differential measurement for $\Delta R(l,\gamma)$.

The correlation matrix of statistical uncertainties for the unfolded differential measurement for $\Delta R(l,\gamma)$.

The correlation matrix of systematic uncertainties for the unfolded differential measurement for $p_{T}(\gamma)$.

The correlation matrix of systematic uncertainties for the unfolded differential measurement for $p_{T}(\gamma)$.

The correlation matrix of systematic uncertainties for the unfolded differential measurement for $|\eta(\gamma)|$.

The correlation matrix of systematic uncertainties for the unfolded differential measurement for $|\eta(\gamma)|$.

The correlation matrix of systematic uncertainties for the unfolded differential measurement for $\Delta R(l,\gamma)$.

The correlation matrix of systematic uncertainties for the unfolded differential measurement for $\Delta R(l,\gamma)$.

Summary of the one-dimensional intervals at 68 and 95% CL.

Summary of the one-dimensional intervals at 68 and 95% CL.

The observed and predicted post-fit yields for the combined Run 2 data set in the SR3 signal region for the electron channel.

The observed and predicted post-fit yields for the combined Run 2 data set in the SR3 signal region for the electron channel.

The observed and predicted post-fit yields for the combined Run 2 data set in the SR3 signal region for the muon channel.

The observed and predicted post-fit yields for the combined Run 2 data set in the SR3 signal region for the muon channel.

The observed and predicted post-fit yields for the combined Run 2 data set in the SR4p signal region for the electron channel.

The observed and predicted post-fit yields for the combined Run 2 data set in the SR4p signal region for the electron channel.

The observed and predicted post-fit yields for the combined Run 2 data set in the SR4p signal region for the muon channel.

The observed and predicted post-fit yields for the combined Run 2 data set in the SR4p signal region for the muon channel.

Negative log-likelihood ratio values with respect to the best fit value of the one-dimensional profiled scan for the Wilson coefficient $c_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the one-dimensional profiled scan for the Wilson coefficient $c_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the one-dimensional profiled scan for the Wilson coefficient $c^{I}_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the one-dimensional profiled scan for the Wilson coefficient $c^{I}_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the one-dimensional scan for the Wilson coefficient $c_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the one-dimensional scan for the Wilson coefficient $c_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the one-dimensional scan for the Wilson coefficient $c^{I}_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the one-dimensional scan for the Wilson coefficient $c^{I}_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the two-dimensional scan for the Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$.

Negative log-likelihood ratio values with respect to the best fit value of the two-dimensional scan for the Wilson coefficients $c_{tZ}$ and $c^{I}_{tZ}$.

Detailed list of the contribution of each source of uncertainty to the total uncertainty on the measured values of $\sigma(tq)$, $\sigma(\bar{t}q)$, $R_t$, and $\sigma(tq+\bar{t}q)$. The evaluation of the systematic uncertainties has a statistical uncertainty of $0.3\,\%$. Uncertainties contributing less than $1.0\,\%$ are marked with "$<1$" in the paper. To provide numerical values for this table in HEPdata, these uncertainties are approximated with $\pm 0.5\,\%$. This approximation is applied to all measurements for the following uncertainties$:$ JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by-jets, JES pileup, $b$-JES, jet vertex fraction, mistag efficiency and $W+\;$jets shape variation. For the measurement of $\sigma(tq)$ the approximation is applied in addition to the following uncertainties$:$ JES flavor response, $c$-tagging efficiency, $t\bar{t}$ generator + parton shower and $t\bar{t}$ ISR/FSR. For the measurement of $\sigma(\bar{t}q)$ the approximation is applied in addition to these uncertainties$:$ JES flavor response, $b/\bar{b}$ acceptance, and $t\bar{t}$ ISR/FSR. For the measurement of $R_t$ the approximation is applied in addition to these uncertainties$:$ JES detector, $b$-tagging efficiency, $c$-tagging efficiency, $b/\bar{b}$ acceptance and $tq$ scale variations. For the measurement of $\sigma(tq+\bar{t}q)$ the approximation is applied in addition to these uncertainties$:$ JES flavor response, $c$-tagging efficiency, $b/\bar{b}$ acceptance, $t\bar{t}$ generator + parton shower and $t\bar{t}$ ISR/FSR.

Differential t-channel top-quark production cross sections and normalized differential t-channel top-quark production cross sections as functions of ABS(YRAP(T)).

The cross sections for top-quark and top-antiquark production in the t-channel, together with the cross-section ratio.

Differential t-channel top-quark production cross sections and normalized differential t-channel top-quark production cross sections as functions of ABS(YRAP(TBAR)).

Parametrization factors for the $m_{t}$ dependence [see Eq. (4) in the paper] of $\sigma(tq)$, $\sigma(\bar tq)$, and $\sigma(t q+\bar t q)$.

The cross sections for top-quark and top-antiquark production in the t-channel, together with the cross-section ratio.

The value of $|V_{tb}|^{2}$ is extracted by dividing the measured value of $\sigma(t q+\bar t q)$ by the prediction of the approximate NNLO calculation. The experimental and theoretical uncertainties are added in quadrature.

Event yields for the 2-jet-$\ell^{+}$ and 2-jet-$\ell^{-}$ HPR channels. The expectation for the signal and background yields correspond to the result of the binned maximum-likelihood fit described in Sec. $\mathrm{VI~D}$ in the paper. The uncertainty of the expectations is the normalization uncertainty of each process after the fit, as described in Sec. $\mathrm{VII~F}$ in the paper.

Migration matrix relating the parton level $p_{\mathrm{T}}(t)$ shown on the $y$ axis and the reconstruction level $p_{\mathrm{T}}(\ell \nu b)$ shown on the $x$ axis for the top-quark $p_{\mathrm{T}}$ distribution.

Migration matrix relating the parton level $p_{\mathrm{T}}(\bar t)$ shown on the $y$ axis and the reconstruction level $p_{\mathrm{T}}(\ell \nu b)$ shown on the $x$ axis for the top-antiquark $p_{\mathrm{T}}$ distribution.

Migration matrix relating the parton level $|y(t)|$ shown on the $y$ axis and the reconstruction level $|y(\ell \nu b)|$ shown on the $x$ axis for the top-quark $|y|$ distribution.

Migration matrix relating the parton level $|y(\bar t)|$ shown on the $y$ axis and the reconstruction level $|y(\ell \nu b)|$ shown on the $x$ axis for the top-antiquark $|y|$ distribution.

Differential t-channel top-quark production cross section as a function of $p_{\mathrm{T}}(t)$ with the uncertainties for each bin given in percent.

Differential t-channel top-quark production cross section as a function of $p_{\mathrm{T}}(\bar t)$ with the uncertainties for each bin given in percent.

Differential t-channel top-quark production cross section as a function of $|y(t)|$ with the uncertainties for each bin given in percent.

Differential t-channel top-quark production cross section as a function of $|y(\bar t)|$ with the uncertainties for each bin given in percent.

Normalized differential t-channel top-quark production cross section as a function of $p_{\mathrm{T}}(t)$ with the uncertainties for each bin given in percent.

Normalized differential t-channel top-quark production cross section as a function of $p_{\mathrm{T}}(\bar t)$ with the uncertainties for each bin given in percent.

Normalized differential t-channel top-quark production cross section as a function of $|y(t)|$ with the uncertainties for each bin given in percent.

Normalized differential t-channel top-quark production cross section as a function of $|y(\bar t)|$ with the uncertainties for each bin given in percent.

Comparison between the measured differential cross sections and the predictions from the NLO calculation using the MSTW2008 PDF set. For each variable and prediction a $\chi^{2}$ value is calculated with HERAfitter using the covariance matrix of each measured spectrum. The theory uncertainties of the predictions are treated as uncorrelated. The number of degrees of freedom (NDF) is equal to the number of bins in the measured spectrum.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}p_{\mathrm{T}}(t)}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, jet-vertex fraction, $b/\bar{b}$ acceptance, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, and $t \bar{t}$ generator. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}p_{\mathrm{T}}(\bar t)}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, mistag efficiency, $b/\bar{b}$ acceptance, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, and $t \bar{t}$ generator. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, jet-vertex fraction, $b/\bar{b}$ acceptance, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(\bar t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, $b/\bar{b}$ acceptance, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{1}{\sigma}\dfrac{\mathrm{d}\sigma}{\mathrm{d}p_{\mathrm{T}}(t)}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The JES $\eta$ intercalibration uncertainty has a sign switch from the first to the second bin. For the $tq$ generator $+$ parton shower uncertainty a sign switch is denoted with $\mp$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, $b/\bar{b}$ acceptance, $c-$tagging efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, lepton uncertainties, $W+$ jets shape variation, and $t \bar{t}$ generator. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{1}{\sigma}\dfrac{\mathrm{d}\sigma}{\mathrm{d}p_{\mathrm{T}}(\bar t)}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. Sign switches within one uncertainty are denoted with $\mp$ and $\pm$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, $b/\bar{b}$ acceptance, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, lepton uncertainties, $W+$ jets shape variation, and $t \bar{t}$ generator. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{1}{\sigma}\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. Sign switches within one uncertainty are denoted with $\mp$ and $\pm$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, b-JES, jet-vertex fraction, $b/\bar{b}$ acceptance, $b-$tagging efficiency, $c-$tagging efficiency, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, lepton uncertainties, $W+$jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{1}{\sigma}\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(\bar t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. Sign switches within one uncertainty are denoted with $\mp$ and $\pm$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content $:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, b-JES, jet energy resolution, jet-vertex fraction, $b/\bar{b}$ acceptance, $b-$tagging efficiency, $c-$ tagging efficiency, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, lepton uncertainties, $W+$ jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Statistical correlation matrices for the differential cross section as a function of $p_{\mathrm{T}}(t)$.

Statistical correlation matrices for the differential cross section as a function of $p_{\mathrm{T}}(\bar t)$.

Statistical correlation matrices for the differential cross section as a function of $|y(t)|$.

Statistical correlation matrices for the differential cross section as a function of $|y(\bar t)|$.

Statistical correlation matrices for the normalized differential cross section as a function of $p_{\mathrm{T}}(t)$.

Statistical correlation matrices for the normalized differential cross section as a function of $p_{\mathrm{T}}(\bar t)$.

Statistical correlation matrices for the normalized differential cross section as a function of $|y(t)|$.

Statistical correlation matrices for the normalized differential cross section as a function of $|y(\bar t)|$.

Experimental uncertainties affecting transfer factors in the analysis that is used to estimate the W background in the signal region (SR). The number of W boson events are denoted as $W_{SR}$ for the SR and in analogy as $W_{\mu\nu}$ ($W_{e\nu}$) for the single-muon (single-electron) control region (CR).

Uncertainties assigned to the simulation based processes in SR and CRs.

Uncertainties assigned to the simulation based processes in SR and CRs.

Cross sections (fb) at large Z $p_{T}$ values in the Z -> $\ell\ell$ and Z -> $\nu\nu$ channels, and their combination. The theoretical predictions from Madgraph at NLO in QCD and corrected to NLO in electroweak using the NNPDF 3.0 are also reported. With the exception of the largest Z $p_{T}$ bin, the statistical uncertainties in the measurements are much smaller than the systematic uncertainties. Both measurements and predictions correspond to $\sigma \mathcal{B}(Z -> \ell\ell)$, where $\sigma$ is the total fiducial cross section, $\mathcal{B}$ is the branching fraction, and $\ell$ is a charged lepton. The $Z -> \nu\nu$ measurement corresponds to $\sigma \mathcal{B}(Z -> \ell\ell)/\mathcal{B}(Z -> \nu\nu)$.

Cross sections (fb) at large Z $p_{T}$ values in the Z -> $\ell\ell$ and Z -> $\nu\nu$ channels, and their combination. The theoretical predictions from Madgraph at NLO in QCD and corrected to NLO in electroweak using the NNPDF 3.0 are also reported. With the exception of the largest Z $p_{T}$ bin, the statistical uncertainties in the measurements are much smaller than the systematic uncertainties. Both measurements and predictions correspond to $\sigma \mathcal{B}(Z -> \ell\ell)$, where $\sigma$ is the total fiducial cross section, $\mathcal{B}$ is the branching fraction, and $\ell$ is a charged lepton. The $Z -> \nu\nu$ measurement corresponds to $\sigma \mathcal{B}(Z -> \ell\ell)/\mathcal{B}(Z -> \nu\nu)$.

Cross sections normalized to the total cross section measurements at high Z $p_{T}$ values in the Z -> $\ell\ell$ and Z -> $\nu\nu$ channels, and in their combination. The uncertainty includes both statistical and systematic uncertainties.

Cross sections normalized to the total cross section measurements at high Z $p_{T}$ values in the Z -> $\ell\ell$ and Z -> $\nu\nu$ channels, and in their combination. The uncertainty includes both statistical and systematic uncertainties.

The relative statistical and systematic uncertainties from various sources for the absolute cross section measurements in bins of Z $p_{T}$ on charged leptons.

The relative statistical and systematic uncertainties from various sources for the absolute cross section measurements in bins of Z $p_{T}$ on charged leptons.

The relative statistical and systematic uncertainties from various sources for the relative cross section measurements in bins of Z $p_{T}$ on charged leptons.

The relative statistical and systematic uncertainties from various sources for the relative cross section measurements in bins of Z $p_{T}$ on charged leptons.

The relative statistical and systematic uncertainties from various sources for the absolute cross section measurements in bins of Z $p_{T}$ on neutrinos.

The relative statistical and systematic uncertainties from various sources for the absolute cross section measurements in bins of Z $p_{T}$ on neutrinos.

The relative statistical and systematic uncertainties from various sources for the relative cross section measurements in bins of Z $p_{T}$ on neutrinos.

The relative statistical and systematic uncertainties from various sources for the relative cross section measurements in bins of Z $p_{T}$ on neutrinos.

The relative statistical and systematic uncertainties from various sources for the absolute cross section measurements in bins of Z $p_{T}$ on neutrinos and charged leptons.

The relative statistical and systematic uncertainties from various sources for the absolute cross section measurements in bins of Z $p_{T}$ on neutrinos and charged leptons.

The relative statistical and systematic uncertainties from various sources for the relative cross section measurements in bins of Z $p_{T}$ on neutrinos and charged leptons.

The relative statistical and systematic uncertainties from various sources for the relative cross section measurements in bins of Z $p_{T}$ on neutrinos and charged leptons.

Cross section measurements in bins of Z $p_{T}$ on neutrinos and charged leptons.

Cross section measurements in bins of Z $p_{T}$ on neutrinos and charged leptons.

The normalized particle-level $\mathrm{t}\overline{\mathrm{t}}$ differential cross section in the fiducial region as a function of the XCone-jet mass.

The normalized particle-level $\mathrm{t}\overline{\mathrm{t}}$ differential cross section in the fiducial region as a function of the XCone-jet mass.

The covariance matrix containing the statistical uncertainties of Figure 2a is shown.

The covariance matrix containing the statistical uncertainties of Figure 2a is shown.

The covariance matrix containing the experimental uncertainties of Figure 2a is shown.

The covariance matrix containing the experimental uncertainties of Figure 2a is shown.

The covariance matrix containing the model uncertainties of Figure 2a is shown.

The covariance matrix containing the model uncertainties of Figure 2a is shown.

The covariance matrix containing the statistical uncertainties of Figure 2b is shown.

The covariance matrix containing the statistical uncertainties of Figure 2b is shown.

The covariance matrix containing the experimental uncertainties of Figure 2b is shown.

The covariance matrix containing the experimental uncertainties of Figure 2b is shown.

The covariance matrix containing the model uncertainties of Figure 2b is shown.

The covariance matrix containing the model uncertainties of Figure 2b is shown.

Summary of typical systematic uncertainties for the signal.

Numbers of observed events for all signal regions, including predicted background contributions and expected signal yields.

Limits on anomalous quartic couplings at 95% CL.

Expected and observed 95% CL upper limits on the cross section times the branching ratio sigma(P P --> W+- a)B(a --> W+- W-+) as a function of ALP mass

Expected and observed 95% CL upper limits on the photophobic ALP model parameter 1/f_a as a function of ALP mass.

Observed signal strength, and observed signal cross section.

Distributions of $\min[M(\ell,\mathrm{j})]$ in the W+jets control region, for 0 W-tagged jet category. Example signal distributions are also shown. The background distributions correspond to background-only fit to data while signal distributions are before the fit to data. Electron and muon event samples are combined. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{j})]$ in the W+jets control region, for 1 or more W-tagged jet category. Example signal distributions are also shown. The background distributions correspond to background-only fit to data while signal distributions are before the fit to data. Electron and muon event samples are combined. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 0 t-tagged jets, 0 W-tagged jets, and 1 b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 0 t-tagged jets, 0 W-tagged jets, and 2 or more b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 0 t-tagged jets, 1 or more W-tagged jets, and 1 b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 0 t-tagged jets, 1 or more W-tagged jets, and 2 or more b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 1 or more t-tagged jets, 0 W-tagged jets, and 1 b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 1 or more t-tagged jets, 0 W-tagged jets, and 2 or more b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 1 or more t-tagged jets, 1 or more W-tagged jets, and 1 b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Distributions of $\min[M(\ell,\mathrm{b})]$ in events with 1 or more t-tagged jets, 1 or more W-tagged jets, and 2 or more b-tagged jets for the combined electron and muon samples in the signal region. Example signal distributions are also shown. The background distributions correspond to the background-only fit to data, while signal distributions are before the fit to data. The last bin includes overflow events and its content is divided by the bin width. The distributions in each category have variable-size bins, chosen so that the statistical uncertainty in the total background in each bin is less than 30%. The lower panel in each plot shows the difference between the observed and the predicted numbers of events in that bin divided by the total uncertainty. The total uncertainty is calculated as the sum in quadrature of the statistical uncertainty in the observed measurement and the statistical and systematic uncertainties in the background-only fit to data.

Expected and observed limits at 95% CL for an LH $X_{5/3}$ after combining all categories for the same-sign dilepton final state. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an RH $X_{5/3}$ after combining all categories for the same-sign dilepton final state. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an LH $X_{5/3}$ after combining all categories for the single-lepton final state. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an RH $X_{5/3}$ after combining all categories for the single-lepton final state. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an LH $X_{5/3}$ after combining the same-sign dilepton and single-lepton final states. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Expected and observed limits at 95% CL for an RH $X_{5/3}$ after combining the same-sign dilepton and single-lepton final states. The theoretical uncertainty in the signal cross section is shown as a narrow band around the theoretical prediction.

Lower mass exclusion limits for scalar and vector LQs.

Observed upper limits on the production cross section for pair production of LQs decaying into a top quark and a muon or bottom quark and a neutrino at 95% CL in the $M_{LQ} - B(LQ \rightarrow t\mu)$ plane.

Lower mass exclusion limits for scalar and vector LQs.

Invariant mass distribution of the selected $\mathrm{B}^+\mathrm{K}^-$ candidates in data

Invariant mass distribution of the selected $\mathrm{B}^0 \mathrm{K}^{0}_{\mathrm{S}}$ candidates in data

The observed signal yields ($N$), natural widths ($\Gamma$), and mass differences from the fits to the $m_{\mathrm{BK}}$ distributions in data, for the two channels: $\mathrm{B}^+\mathrm{K}^-$ and $\mathrm{B}^0 \mathrm{K}^{0}_{\mathrm{S}}$. The uncertainties are statistical only.

The measured ratios of branching fractions

The measured ratios of cross sections times branching fractions

The measured mass differences $M(\mathrm{B}^{(*)0}_{\mathrm{s1,2}})-M^{\mathrm{PDG}}_{\mathrm{B}}-M^{\mathrm{PDG}}_{\mathrm{K}}$

The measured masses of $\mathrm{B}^{*}_{\mathrm{s2}}(5840)^0$ and $\mathrm{B}_{\mathrm{s1}}(5830)^0$ mesons

The measured mass differences between neutral and charged B mesons

The measured $\mathrm{B}^{*}_{\mathrm{s2}}(5840)^0$ natural width

Median expected and observed 95% CL upper limits on the cross-section times branching ratio (sigma*B) for W'_SSM production for the exclusive muon and electron channels, and for both channels combined.

Expected 95% CL exclusion contour for the Gbb signal.

Observed 95% CL exclusion contour for the Gbb signal.

Expected 95% CL exclusion contour for the Gtt combination.

Observed 95% CL exclusion contour for the Gtt combination.

Acceptances for the Gbb model in SR-Gbb-A. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gbb model in SR-Gbb-B. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gbb model in SR-Gbb-C. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-0L-A. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-0L-B. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-0L-C. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-1L-A. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptances for the Gtt model in SR-Gtt-1L-B. Acceptance is evaluated at truth level, with only leptons from heavy bosons and taus considered, and no further quality or isolation criteria applied in their selection.

Acceptance times efficiency for the Gbb model in SR-Gbb-A.

Acceptance times efficiency for the Gbb model in SR-Gbb-B.

Acceptance times efficiency for the Gbb model in SR-Gbb-C.

Acceptance times efficiency for the Gtt model in SR-Gtt-0L-A.

Acceptance times efficiency for the Gtt model in SR-Gtt-0L-B.

Acceptance times efficiency for the Gtt model in SR-Gtt-0L-C.

Acceptance times efficiency for the Gtt model in SR-Gtt-1L-A.

Acceptance times efficiency for the Gtt model in SR-Gtt-1L-B.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gbb model in SR-Gbb-A.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gbb model in SR-Gbb-B.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gbb model in SR-Gbb-C.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-0L-A.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-0L-B.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-0L-C.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-1L-A.

95% CL upper limit on the cross-section times branching ratio (in fb) for the Gtt model in SR-Gtt-1L-B.

Signal region yielding the best expected sensitivity for each point of the parameter space in the Gbb model.

Signal region yielding the best expected sensitivity for each point of the parameter space in the Gtt model for the 0-lepton channel.

Signal region yielding the best expected sensitivity for each point of the parameter space in the Gtt model for the 1-lepton channel.

Combination of two 0-lepton and 1-lepton signal regions yielding the best expected sensitivity for each point of the parameter space in the Gtt model.

Comparison of data with estimated backgrounds in the $\tilde{E}_\text{T}^\text{miss}$ distribution with the TZCR1 event selection except for the requirement on $\tilde{E}_\text{T}^\text{miss}$. The variables $\tilde{E}_\text{T}^\text{miss}$ and $\tilde{m}_\text{T}$ are constructed in the same way as $E_\text{T}^\text{miss}$ and $m_\text{T}$ but treating the leading photon transverse momentum as invisible. The predicted backgrounds are scaled with normalization factors. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflow.

Comparison of data with estimated backgrounds in the $\tilde{m}_\text{T}$ distribution with the TZCR1 event selection except for the requirement on $\tilde{m}_\text{T}$. The variables $\tilde{E}_\text{T}^\text{miss}$ and $\tilde{m}_\text{T}$ are constructed in the same way as $E_\text{T}^\text{miss}$ and $m_\text{T}$ but treating the leading photon transverse momentum as invisible. The predicted backgrounds are scaled with normalization factors. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflow.

Comparison of the observed data ($n_\text{obs}$) with the predicted background ($n_\text{exp}$) in the validation and signal regions. The background predictions are obtained using the background-only fit configuration. The bottom panel shows the significance of the difference between data and predicted background, where the significance is based on the total uncertainty ($\sigma_\text{tot}$).

Jet multiplicity distributions for events where exactly two signal leptons are selected. No correction factors are included in the background normalizations. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflow.

Jet multiplicity distributions for events where exactly one lepton plus one $\tau$ candidate are selected. No correction factors are included in the background normalizations. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflow.

The $E_\text{T}^\text{miss}$ distribution in SR1. In the plot, the full event selection in the corresponding signal region is applied, except for the requirement on $E_\text{T}^\text{miss}$. The predicted backgrounds are scaled with normalization factors. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin contains the overflow. Benchmark signal models are overlaid for comparison. The benchmark models are specified by the gluino and stop masses, given in TeV in the table.

The $m_\text{T}$ distribution in SR1. In the plot, the full event selection in the corresponding signal region is applied, except for the requirement on $m_\text{T}$. The predicted backgrounds are scaled with normalization factors. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin contains the overflow. Benchmark signal models are overlaid for comparison. The benchmark models are specified by the gluino and stop masses, given in TeV in the table.

Expected (black dashed) 95% excluded regions in the plane of $m_{\tilde{g}}$ versus $m_{\tilde{t}_1}$ for gluino-mediated stop production.

Observed (red solid) 95% excluded regions in the plane of $m_{\tilde{g}}$ versus $m_{\tilde{t}_1}$ for gluino-mediated stop production.

Expected (black dashed) 95% excluded regions in the plane of $m_{\tilde{t}_1}$ versus $m_{\tilde{\chi}_1^0}$ for direct stop production.

Observed (red solid) 95% excluded regions in the plane of $m_{\tilde{t}_1}$ versus $m_{\tilde{\chi}_1^0}$ for direct stop production.

The expected upper limits on $T$ quark pair production times the squared branching ratio for $T \rightarrow tZ$ as a function of the $T$ quark mass.

The observed upper limits on $T$ quark pair production times the squared branching ratio for $T \rightarrow tZ$ as a function of the $T$ quark mass.

The expected limits on $T$ quarks as a function of the branching ratios $B\left(T \rightarrow bW\right)$ and $B\left(T \rightarrow tH\right)$ for a $T$ quark with a mass of 800 GeV. The $T$ is assumed to decay in three possible ways: $T \to tZ$, $T \to tH$, and $T \to bW$.

The observed limits on $T$ quarks as a function of the branching ratios $B\left(T \rightarrow bW\right)$ and $B\left(T \rightarrow tH\right)$ for a $T$ quark with a mass of 800 GeV. The $T$ is assumed to decay in three possible ways: $T \to tZ$, $T \to tH$, and $T \to bW$.

The $m_\text{T}$ distribution in the WVR2-tail validation region which has the same preselection and jet $p_\text{T}$ requirements as SR2.

The $am_\text{T2}$ distribution in the WVR2-tail validation region which has the same preselection and jet $p_\text{T}$ requirements as SR2.

Large-radius jet mass ($R=1.2$), decomposed into the number of small-radius jet constituents. The lower panel shows the ratio of the total data to the total prediction (summed over all jet multiplicities). Events are required to have one lepton, four jets with $p_\text{T}>80,50,40,40$ GeV, at least one $b$-tagged jet, $E_\text{T}^\text{miss}>200$ GeV, and $m_\text{T}>30$ GeV.

Distribution of $m_\text{T2}^\tau$ in data for a selection enriched in $t\bar{t}$ events with one hadronically decaying $\tau$. Events that have no hadronic $\tau$ candidate (that passes the Loose identification criteria, as well as other requirements) are not shown in the plot.

Upper limits on the model cross-section in units of pb for the gluino-mediated stop models.

Upper limits on the model cross-section in units of pb for the models with direct stop pair production.

Illustration of the best expected signal region per signal grid point for the gluino-mediated stop models. This mapping is used for the final combined exclusion limits.

Illustration of the best expected signal region per signal grid point for models with direct stop pair production. This mapping is used for the final combined exclusion limits.

Expected $CL_s$ values for the gluino-mediated stop models.

Observed $CL_s$ values for the gluino-mediated stop models.

Expected $CL_s$ values for the direct stop pair production models.

Observed $CL_s$ values for the direct stop pair production models.

Expected limit using SR1 for models with direct stop pair production and an unpolarized stop (and bino LSP).

Expected limit using SR1 for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Expected limit using SR1 for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Observed limit using SR1 for models with direct stop pair production and an unpolarized stop (and bino LSP).

Observed limit using SR1 for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Observed limit using SR1 for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Expected limit using SR2 for models with direct stop pair production and an unpolarized stop (and bino LSP).

Expected limit using SR2 for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Expected limit using SR2 for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Observed limit using SR2 for models with direct stop pair production and an unpolarized stop (and bino LSP).

Observed limit using SR2 for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Observed limit using SR2 for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Expected limit using SR1+SR2 (best expected) for models with direct stop pair production and an unpolarized stop (and bino LSP).

Expected limit using SR1+SR2 (best expected) for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Expected limit using SR1+SR2 (best expected) for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Observed limit using SR1+SR2 (best expected) for models with direct stop pair production and an unpolarized stop (and bino LSP).

Observed limit using SR1+SR2 (best expected) for models with direct stop pair production with $\tilde{t}_1=\tilde{t}_L$ (and bino LSP).

Observed limit using SR1+SR2 (best expected) for models with direct stop pair production with $\tilde{t}_1\sim\tilde{t}_R$ (and bino LSP).

Acceptance for SR1 in the gluino-mediated stop models. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR1 in the direct stop pair production. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR2 in the gluino-mediated stop models. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR2 in the direct stop pair production. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR3 in the gluino-mediated stop models. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Acceptance for SR3 in the direct stop pair production. The acceptance is defined as the fraction of signal events that pass the analysis selection performed on generator-level objects, therefore emulating an ideal detector with perfect particle identification and no measurement resolution effects.

Efficiency for SR1 in the gluino-mediated stop models. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR1 in the direct stop pair production. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR2 in the gluino-mediated stop models. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR2 in the direct stop pair production. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR3 in the gluino-mediated stop models. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

Efficiency for SR3 in the direct stop pair production. The efficiency is the ratio between the expected signal rate calculated with simulated data passing all the reconstruction level cuts applied to reconstructed objects, and the signal rate for an ideal detector (with perfect particle identification and no measurement resolution effects).

The distribution of the missing transverse momentum is shown in soft-lepton 2-jet W+jets control regions after normalising the ttbar and W+jets background processes in the simultaneous fit.

Expected background yields as obtained in the background-only fits in all hard-lepton and soft-lepton validation together with observed data are given. Uncertainties in the fitted background estimates combine statistical (in the simulated event yields) and systematic uncertainties.

Expected background yields as obtained in the background-only fits in all hard-lepton and soft-lepton signal together with observed data are given. Uncertainties in the fitted background estimates combine statistical (in the simulated event yields) and systematic uncertainties.

Distributions of mt for the hard-lepton 4-jet low-x signal region. The requirement on the variable plotted is removed from the definitions of the signal regions, where the arrow indicates the position of the cut in the signal region. The lower panels of the plots show the ratio of the observed data to the total background prediction as derived in the background-only fit. The uncertainty bands plotted include all statistical and systematic uncertainties as discussed in Section 7. The component `Others' is the sum of Z+jets and ttbar+V. The last bin includes the overflow.

Distributions of met/meff for the 4-jet high-x signal region. The requirement on the variable plotted is removed from the definitions of the signal regions, where the arrow indicates the position of the cut in the signal region. The lower panels of the plots show the ratio of the observed data to the total background prediction as derived in the background-only fit. The uncertainty bands plotted include all statistical and systematic uncertainties as discussed in Section 7. The component `Others' is the sum of Z+jets and ttbar+V. The last bin includes the overflow.

Distributions of mt for the hard-lepton 5-jet signal region. The requirement on the variable plotted is removed from the definitions of the signal regions, where the arrow indicates the position of the cut in the signal region. The lower panels of the plots show the ratio of the observed data to the total background prediction as derived in the background-only fit. The uncertainty bands plotted include all statistical and systematic uncertainties as discussed in Section 7. The component `Others' is the sum of Z+jets and ttbar+V. The last bin includes the overflow.

Distributions of mt for the hard-lepton 6-jet signal region. The requirement on the variable plotted is removed from the definitions of the signal regions, where the arrow indicates the position of the cut in the signal region. The lower panels of the plots show the ratio of the observed data to the total background prediction as derived in the background-only fit. The uncertainty bands plotted include all statistical and systematic uncertainties as discussed in Section 7. The component `Others' is the sum of Z+jets and ttbar+V. The last bin includes the overflow.

Distributions of met for the soft-lepton 2-jet signal region. The requirement on the variable plotted is removed from the definitions of the signal regions, where the arrow indicates the position of the cut in the signal region. The lower panels of the plots show the ratio of the observed data to the total background prediction as derived in the background-only fit. The uncertainty bands plotted include all statistical and systematic uncertainties as discussed in Section 7. The component `Others' is the sum of Z+jets and ttbar+V. The last bin includes the overflow.

Distributions of met for the soft-lepton 5-jet signal region. The requirement on the variable plotted is removed from the definitions of the signal regions, where the arrow indicates the position of the cut in the signal region. The lower panels of the plots show the ratio of the observed data to the total background prediction as derived in the background-only fit. The uncertainty bands plotted include all statistical and systematic uncertainties as discussed in Section 7. The component `Others' is the sum of Z+jets and ttbar+V. The last bin includes the overflow.

The observed combined 95% CL exclusion limits in the the gluino simplified models using for each model point the signal region with the best expected sensitivity. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The expected combined 95% CL exclusion limits in the the gluino simplified models using for each model point the signal region with the best expected sensitivity. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The yellow band ($+ 1 \sigma$) of the combined 95% CL exclusion limits in the the gluino simplified models using for each model point the signal region with the best expected sensitivity. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models. The yellow band represents the $\pm 1 \sigma$ variation of the median expected limit due to the experimental and theoretical uncertainties.

The yellow band ($- 1 \sigma$) of the combined 95% CL exclusion limits in the the gluino simplified models using for each model point the signal region with the best expected sensitivity. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models. The yellow band represents the $\pm 1 \sigma$ variation of the median expected limit due to the experimental and theoretical uncertainties.

The observed combined 95% CL exclusion limits in the the gluino simplified models using for each model point the signal region with the best expected sensitivity. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The expected combined 95% CL exclusion limits in the the gluino simplified models using for each model point the signal region with the best expected sensitivity. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The yellow band ($+ 1 \sigma$) of the combined 95% CL exclusion limits in the the gluino simplified models using for each model point the signal region with the best expected sensitivity. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$. The yellow band represents the $\pm 1 \sigma$ variation of the median expected limit due to the experimental and theoretical uncertainties.

The yellow band ($- 1 \sigma$) of the combined 95% CL exclusion limits in the the gluino simplified models using for each model point the signal region with the best expected sensitivity. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$. The yellow band represents the $\pm 1 \sigma$ variation of the median expected limit due to the experimental and theoretical uncertainties.

The observed limits for the soft-lepton 2-jet signal region. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The expected limits for the soft-lepton 2-jet signal region. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The observed limits for the hard-lepton 4-jet low-x signal region. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The expected limits for the hard-lepton 4-jet low-x signal region. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The observed limits for the hard-lepton 5-jet signal region. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The expected limits for the hard-lepton 5-jet signal region. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The observed limits for the hard-lepton 6-jet signal region. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The expected limits for the hard-lepton 6-jet signal region. The limits are presented in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ models.

The observed limits for the soft-lepton 5-jet signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The expected limits for the soft-lepton 5-jet signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The observed limits for the hard-lepton 4-jet low-x signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The expected limits for the hard-lepton 4-jet low-x signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The observed limits for the hard-lepton 4-jet high-x signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The expected limits for the hard-lepton 4-jet high-x signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The observed limits for the hard-lepton 5-jet signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The expected limits for the hard-lepton 5-jet signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The observed limits for the hard-lepton 6-jet signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

The expected limits for the hard-lepton 6-jet signal region. The limits are presented in the (gluino, x) plane for the chargino = 60 GeV models where $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

Number of generated events in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$.

Number of generated events in the (gluino, x) plane for the chargino = 60 GeV models.

Production cross-section in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$.

Production cross-section in the (gluino, x) plane for the chargino = 60 GeV models.

Acceptance times efficiency obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 4-jet low-x).

Acceptance times efficiency in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 4-jet high-x).

Acceptance times efficiency in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 5-jet).

Acceptance times efficiency in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 6-jet).

Acceptance times efficiency in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (soft-lepton 2-jet).

Acceptance times efficiency obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 4-jet low-x).

Acceptance times efficiency in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 4-jet high-x).

Acceptance times efficiency in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 5-jet).

Acceptance times efficiency in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 6-jet).

Acceptance times efficiency in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (soft-lepton 5-jet).

The observed CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 4-jet low-x).

The observed CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 4-jet high-x).

The observed CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 5-jet).

The observed CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 6-jet).

The observed CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (soft-lepton 2-jet).

The expected CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 4-jet low-x).

The expected CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 4-jet high-x).

The expected CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 5-jet).

The expected CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (hard-lepton 6-jet).

The expected CLs values as obtained in the different signal regions in the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$ (soft-lepton 2-jet).

The observed CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 4-jet low-x).

The observed CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 4-jet high-x).

The observed CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 5-jet).

The observed CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 6-jet).

The observed CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (soft-lepton 5-jet).

The expected CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 4-jet low-x).

The expected CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 4-jet high-x).

The expected CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 5-jet).

The expected CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (hard-lepton 6-jet).

The expected CLs values as obtained in the different signal regions in the (gluino, x) plane for the chargino = 60 GeV models (soft-lepton 5-jet).

The signal region yielding in the best expected limit is indicated for every signal point used in the the gluino simplified models for the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$.

The signal region yielding in the best expected limit is indicated for every signal point used in the the gluino simplified models for the (gluino, x) mass plane where for the chargino = 60 GeV and $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

Model-dependent 95% CL upper limits on the visible cross-section in addition to the observed and expected exclusion limits for the (gluino, chargino) mass plane for the scenario where the mass of the chargino is fixed to $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1)) = 1/2$.

Model-dependent 95% CL upper limits on the visible cross-section in addition to the observed and expected exclusion limits for the (gluino, x) mass plane where for the chargino = 60 GeV and $x=(m(\tilde\chi^\pm_1)-m(\tilde\chi^0_1))/(m(\tilde g) - m(\tilde\chi^0_1))$.

Simulated background event samples: the corresponding generator, parton shower, cross-section normalisation, PDF set and underlying-event tune are shown.

Overview of the selection criteria for the soft-lepton signal regions. The symbol $p_{T}^{l}$ refers to signal leptons.

Overview of the selection criteria for the hard-lepton signal regions. The symbol $p_{T}^{l}$ refers to signal leptons.

Background fit results for the hard-lepton and soft-lepton signal regions, for an integrated luminosity of 3.2 fb-1. Uncertainties in the fitted background estimates combine statistical (in the simulated event yields) and systematic uncertainties. The uncertainties in this table are symmetrised for propagation purposes but truncated at zero to remain within the physical boundaries.

Breakdown of upper limits. The columns show from left to right: the name of the respective signal region; the 95% confidence level (CL) upper limits on the visible cross-section and on the number of signal events the 95% CL upper limit on the number of signal events, given the expected number (and $\pm 1 \sigma$ variations on the expectation) of background events; the two-sided CLb value, i.e. the confidence level observed for the background-only hypothesis and the one-sided discovery p-value (p(s = 0)). The discovery p-values are capped to 0.5 in the case of observing less events than the fitted background estimates.

Table shows the data, fitted background and expected signal event counts for a benchmark signal point in each bin of the mt distribution shown in figure 5 (top left). The fit results are shown for an integrated luminosity of 3.2 fb-1.

Table shows the data, fitted background and expected signal event counts for a benchmark signal point in each bin of the met/meff distribution shown in figure 5 (top right). The fit results are shown for an integrated luminosity of 3.2 fb-1.

Table shows the data, fitted background and expected signal event counts for a benchmark signal point in each bin of the mt distribution shown in figure 5 (middle left). The fit results are shown for an integrated luminosity of 3.2 fb-1.

Table shows the data, fitted background and expected signal event counts for a benchmark signal point in each bin of the mt distribution shown in figure 5 (middle right). The fit results are shown for an integrated luminosity of 3.2 fb-1.

Table shows the data, fitted background and expected signal event counts for a benchmark signal point in each bin of the met distribution shown in figure 5 (bottom left). The fit results are shown for an integrated luminosity of 3.2 fb-1.

Table shows the data, fitted background and expected signal event counts for a benchmark signal point in each bin of the met distribution shown in figure 5 (bottom right). The fit results are shown for an integrated luminosity of 3.2 fb-1.

Cutflow table for the hard-lepton signal regions with representative target signal models. The weighted numbers are normalized to 3.2 fb-1 and rounded to the statistical error.

Cutflow table for the hard-lepton signal regions with representative target signal models. The weighted numbers are normalized to 3.2 fb-1 and rounded to the statistical error.

Data (bold boxes) and background estimates (colour fill) for m_beta vs. m_betagamma for the gluino R-hadron search (1000 GeV). The blue thin-line boxes illustrate the expected signal (on top of background) for the given R-hadron mass hypothesis. The black dashed vertical/horizontal lines at 500 GeV show the mass selection (signal region in the top-right). Two events pass this selection.

Expected (dashed black line) and observed (solid red line) 95% CL upper limits on the cross section as a function of mass for the production of long-lived gluino R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. The observed 8 TeV Run-1 limit and theory prediction [arXiv:1411.6795] are shown in dash-dotted and dotted lines, respectively.

Expected (dashed black line) and observed (solid red line) 95% CL upper limits on the cross section as a function of mass for the production of bottom-squark R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. The observed 8 TeV Run-1 limit and theory prediction [arXiv:1411.6795] are shown in dash-dotted and dotted lines, respectively.

Expected (dashed black line) and observed (solid red line) 95% CL upper limits on the cross section as a function of mass for the production of top-squark R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. The observed 8 TeV Run-1 limit and theory prediction [arXiv:1411.6795] are shown in dash-dotted and dotted lines, respectively.

Final selection requirements as a function of the simulated R-hadron mass.

Summary of all studied systematic uncertainties. Ranges indicate a dependency on the R-hadron mass hypothesis (from low to high masses).

Expected signal yield (Nsig) and efficiency (eff.), estimated background (Nbkg) and observed number of events in data (Nobs) for the full mass range after the final selection using 3.2/fb of data. The stated uncertainties include both the statistical and systematic contribution.

Distribution of the truth-level beta for gluino R-hadrons in exemplary signal MC samples and muons in a Zmumu MC sample. All distributions have been normalised to one. The last bin contains the overflow of the histograms. The distributions illustrate the good discriminating power of the variables.

Distribution of the truth-level betagamma for gluino R-hadrons in exemplary signal MC samples and muons in a Zmumu MC sample. All distributions have been normalised to one. The last bin contains the overflow of the histograms. The distributions illustrate the good discriminating power of the variables.

Expected (dashed black line) and observed (solid red line) 95% confidence level upper limits on the cross section as a function of mass for the production of long-lived gluino R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. For meta-stable gluinos with a lifetime of 50 ns. (mass exclusion: about 1660 GeV expected, 1520 GeV observed).

Expected (dashed black line) and observed (solid red line) 95% confidence level upper limits on the cross section as a function of mass for the production of long-lived gluino R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. For meta-stable gluinos with a lifetime of 30 ns. (mass exclusion: about 1660 GeV expected, 1520 GeV observed).

Expected (dashed black line) and observed (solid red line) 95% confidence level upper limits on the cross section as a function of mass for the production of long-lived gluino R-hadrons. The theory prediction along with its +-1sigma uncertainty is show as a black line and a blue band, respectively. For meta-stable gluinos with a lifetime of 10 ns. (mass exclusion: about 1660 GeV expected, 1520 GeV observed).

Object-quality selection cut-flow with observed data and exemplary expected events (scaled to 3.2/fb for MC) in the gluino R-hadron search.

Object-quality selection cut-flow with observed data and exemplary expected events (scaled to 3.2/fb for MC) in the squark R-hadron search.

Expected signal yield (Nsig) and efficiency (eff.), estimated background (Nbkg) and observed number of events in data (Nobs) for the full mass range in the meta-stable gluino R-hadron search using 3.2/fb of data. The stated uncertainties include both the statistical and systematic contribution.

Selection efficiencies in % for the signal in the top squark search, estimated from the simulation.

The final $S_{\text{T}}$ distribution for the leptoquark search with the $\text{e}\tau_{\text{h}}$ and $\mu\tau_{\text{h}}$ channels combined. The last bin contains the overflow events.

The final $S_{\text{T}}$ distribution for the top squark search with the $\text{e}\tau_{\text{h}}$ and $\mu\tau_{\text{h}}$ channels combined. The last bin contains the overflow events.

The expected and observed combined upper limits on the third-generation LQ pair production cross section $\sigma$ times the square of the branching fraction, $\mathcal{B}^2$, at 95% CL, as a function of the LQ mass. These limits also apply to top squarks decaying directly via the coupling $\lambda^{\prime}_{333}$.

The expected and observed combined upper limits on the top squark pair production cross section $\sigma$ times the square of the branching fraction, $\mathcal{B}^2$, at 95% CL, as a function of the top squark mass. These limits apply to top squarks with a chargino-mediated decay through the coupling $\lambda^{\prime}_{3jk}$.

The expected and observed 95% CL upper limits on the branching fraction for the leptoquark decay to a tau lepton and a bottom quark, as a function of the leptoquark mass. A search for top squark pair production [EPJC 73(2013)2677] has the same kinematic signature as the leptoquark decay to a tau neutrino and a top quark. This search is reinterpreted to provide the expected and observed 95% CL upper limits for low values of $\mathcal{B}$, assuming the leptoquark only decays to third-generation fermions.

Number of data events (20.3 fb$^{-1}$), number of predicted events from the fit, statistical uncertainty on the fit, systematic uncertainty on the choice of control region, and on the choice of fit function versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ lower bin edge for inclusive jet multiplicity $N_{\textrm{Jet}} \geq 6$. The total uncertainty is obtained by adding the three uncertainties linearly.

Number of data events (20.3 fb$^{-1}$), number of predicted events from the fit, statistical uncertainty on the fit, systematic uncertainty on the choice of control region, and on the choice of fit function versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ lower bin edge for inclusive jet multiplicity $N_{\textrm{Jet}} \geq 7$. The total uncertainty is obtained by adding the three uncertainties linearly.

Number of data events (20.3 fb$^{-1}$), number of predicted events from the fit, statistical uncertainty on the fit, systematic uncertainty on the choice of control region, and on the choice of fit function versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ lower bin edge for inclusive jet multiplicity $N_{\textrm{Jet}} \geq 8$. The total uncertainty is obtained by adding the three uncertainties linearly.

Upper limits on the visible cross section (cross section ($\sigma$) times acceptance ($A$) times efficiency ($\epsilon$)) at the 95% CL versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ for $N_{\textrm{Jet}}\geq 3$.

Upper limits on the visible cross section (cross section ($\sigma$) times acceptance ($A$) times efficiency ($\epsilon$)) at the 95% CL versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ for $N_{\textrm{Jet}}\geq 4$.

Upper limits on the visible cross section (cross section ($\sigma$) times acceptance ($A$) times efficiency ($\epsilon$)) at the 95% CL versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ for $N_{\textrm{Jet}}\geq 5$.

Upper limits on the visible cross section (cross section ($\sigma$) times acceptance ($A$) times efficiency ($\epsilon$)) at the 95% CL versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ for $N_{\textrm{Jet}}\geq 6$.

Upper limits on the visible cross section (cross section ($\sigma$) times acceptance ($A$) times efficiency ($\epsilon$)) at the 95% CL versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ for $N_{\textrm{Jet}}\geq 7$.

Upper limits on the visible cross section (cross section ($\sigma$) times acceptance ($A$) times efficiency ($\epsilon$)) at the 95% CL versus inclusive $H_{\textrm{T}}^{\textrm{min}}$ for $N_{\textrm{Jet}}\geq 8$.

Mean reconstruction efficiencies $(\epsilon)$ and RMS of the reconstruction efficiencies for various black hole and string ball models.

Missing transverse momentum distribution after SR3b selection, beside the $E_\mathrm{T}^\mathrm{miss}$ requirement. The results in the signal region correspond to the last inclusive bin. The systematic uncertainties include theory uncertainties for the backgrounds with prompt SS/3L and the full systematic uncertainties for data-driven backgrounds. For illustration the distribution for a benchmark SUSY scenario ($pp\to \tilde g\tilde g$, $\tilde g\to t\bar t\tilde\chi_1^0$, $m_{\tilde g}=1.2$ TeV, $m_{\tilde\chi_1^0}=0.7$ TeV) is also shown.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qq(\tilde\ell\ell/\tilde\nu\nu)$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to qqWZ\tilde\chi_1^0$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde b_1$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde b_1\tilde b_1^*$ pair production with exclusive $\tilde b_1\to t\tilde\chi_1^-$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

Observed exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Expected exclusion limits on the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays. All limits are computed at 95% CL.

Upper limits on signal cross-sections as function of the $\tilde g$ and $\tilde\chi_1^0$ masses in the context of SUSY scenarios with simplified mass spectra featuring $\tilde g\tilde g$ pair production with exclusive $\tilde g\to t\bar t\tilde\chi_1^0$ decays, obtained using the signal efficiency and acceptance specific to each model. All limits are computed at 95% CL.

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar q(\tilde\ell\ell/\tilde\nu\nu)$ decay: signal acceptance (in %) in the signal region SR0b3j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar q(\tilde\ell\ell/\tilde\nu\nu)$ decay: reconstruction efficiency (in %) in the signal region SR0b3j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar qWZ\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR0b5j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to q\bar qWZ\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR0b5j. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde b_1\tilde b_1^*$ production and $\tilde b_1\to tW\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR1b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde b_1\tilde b_1^*$ production and $\tilde b_1\to tW\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR1b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to t\bar t\tilde\chi_1^0$ decay: signal acceptance (in %) in the signal region SR3b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

SUSY scenario with $\tilde g\tilde g$ production and $\tilde g\to t\bar t\tilde\chi_1^0$ decay: reconstruction efficiency (in %) in the signal region SR3b. The benchmark scenarios used to set exclusion limits are materialized by black dot markers. Acceptance and efficiency are defined as in appendix A of [JHEP 06 (2014) 124, arXiv: 1403.4853v1 [hep-ex]].

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 10j50 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

Observed 95% CL limit for the pMSSM grid.

Observed 95% CL limit for the pMSSM grid when the signal cross section is increased by one standard deviation.

Observed 95% CL limit for the pMSSM grid when the signal cross section is decreased by one standard deviation.

Expected 95% CL limit for the pMSSM grid.

+1 sigma excursion of the expected 95% CL limit for the pMSSM grid.

-1 sigma excursion of the expected 95% CL limit for the pMSSM grid.

Observed 95% CL limit for the 2Step grid.

Observed 95% CL limit for the 2Step grid when the signal cross section is increased by one standard deviation.

Observed 95% CL limit for the 2Step grid when the signal cross section is decreased by one standard deviation.

Expected 95% CL limit for the 2Step grid.

+1 sigma excursion of the expected 95% CL limit for the 2Step grid.

-1 sigma excursion of the expected 95% CL limit for the 2Step grid.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j50 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j50 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j50 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 9j50 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 9j50 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 9j50 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 10j50 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 10j50 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 10j50 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 7j80 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 7j80 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 7j80 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region 8j80 2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

Degree of multijet closure for signal and vaidation regions with at no b-jet requirement. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The bins labelled in bold are signal regions, while the others are validation regions. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions with at least 1 b-jet. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The bins labelled in bold are signal regions, while the others are validation regions. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions with at least 2 b-jets. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The bins labelled in bold are signal regions, while the others are validation regions. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Summary of all 15 signal regions (post-fit).

Signal region yielding the best-expected CLs value, the best expected CLs value, and the corresponding observed CLs value for the 2Step grid.

Signal region yielding the best-expected CLs value, the best expected CLs value, and the corresponding observed CLs value for the pMSSM grid.

95% CLs observed upper limit on model cross-section for 2-step signal points for the best-expected signal region.

Performance of the 8j50-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-0b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-1b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-2b selection for the pMSSM grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j50-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 9j50-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 10j50-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 7j80-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-0b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-1b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Performance of the 8j80-2b selection for the 2Step grid: number of generated signal events; total signal cross-section; acceptance; efficiency (fractional); observed CL using this region alone; expected CL using this region alone.

Ratio of the Z'NU to Z'SSM acceptance times efficiency (ACC*EFF) in the lep-had channel as a function of sin^2phi and the Z' mass.

Z' SSM acceptance (ACC) and product of acceptance and efficiency (ACC*EFF) for the had-had and lep-had selections as a function of the Z' mass.

Number of expected Z'SSM signal (NS), background (NB) and observed (N) events passing the total transverse mass ($m_{\rm T}^{\rm tot}$) threshold in the had-had and lep-had channels.

Median expected and observed 95% CL upper limits on the cross-section times branching fraction to tau+tau- for Z'SSM production for the exclusive had-had and lep-had channels, and for both channels combined.

Observed 95% CL upper limits on the cross-section times branching fraction to tau+tau- for Z'L and Z'R production for the combination of the had-had and lep-had channels.

Observed 95% CL lower limit on the Z'NU mass as a function of sin^2(phi).