The impact of the eight most important systematic uncertainties on \(R_t\), in %.

The post-fit yields in the two SRs. All uncertainties applied in the analysis are included.

Limits on EFT parameters and Vtb extracted from the analysis. The upper and lower limits correspond to 95% CL.

Results of the main analysis.

Pre-fit distribution of the NN discriminant \(D_{\text{nn}}\) in SR plus.

Post-fit distribution of the NN discriminant \(D_{\text{nn}}\) in SR plus.

Post-fit distribution of the \(\Delta\phi(\vec{p}_{T}^{miss},\mu)\) variable in the muon-plus CR.

Pre-fit distribution of the NN discriminant \(D_{\text{nn}}\) in SR minus.

Post-fit distribution of the NN discriminant \(D_{\text{nn}}\) in SR minus.

Post-fit distribution of the \(\Delta\phi(\vec{p}_{T}^{miss},\mu)\) variable in the muon-minus CR.

Pre-fit distribution of the invariant mass of the untagged jet and the b-tagged jet, \(m(jb)\), in SR plus.

Pre-fit distribution of the absolute value of the pseudorapidity of the untagged jet, \(|\eta(j)|\), in SR plus.

Pre-fit distribution of the absolute value of the difference in \(p_{\text{T}}\) between the reconstructed W boson and the jet pair, \(|\Delta p_{\text{T}}(W, jb)|\), in SR plus.

Pre-fit distribution of the difference in azimuth angle between the reconstructed W boson and the jet pair, \(|\Delta\phi(W, jb)|\), in SR plus.

Pre-fit distribution of the invariant mass of the reconstructed top quark, \(m(t)\), in SR plus.

Pre-fit distribution of the absolute value of the difference in pseudorapidity between the charged lepton and the untagged jet, \(|\Delta\eta(\ell ,j)|\), SR plus.

Pre-fit distribution of the angular distance of the charged lepton and the untagged jet, \(\Delta R(\ell ,j)\), in SR plus.

Pre-fit distribution of the absolute value of the difference in pseudorapidity between the b-tagged jet and the charged lepton, \(|\Delta\eta(b,\ell )|\), in SR plus.

Distribution of the NN output in the $\geq$1fj$\,$SR in data and the expected contribution of the signal and background processes after the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions considering the correlations of the uncertainties as obtained by the fit.

Distribution of the NN output in the $t\bar{t}\gamma\,$CR in data and the expected contribution of the signal and background processes after the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions considering the correlations of the uncertainties as obtained by the fit.

Total event yield in the $W\gamma\,$CR in data and the expected contribution of the signal and background processes after the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions considering the correlations of the uncertainties as obtained by the fit.

Distribution of the scalar sum of the jet transverse momenta in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions. The first and last bins include the underflow and overflow, respectively.

Distribution of the $\eta$ of the $b$-tagged jet in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions.

Distribution of the reconstructed top-quark mass in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions. The first and last bins include the underflow and overflow, respectively.

Distribution of the $p_{\mathrm{T}}$ of the top-quark+photon system in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the photon $p_{\mathrm{T}}$ in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the photon $\eta$ in the 0fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions.

Distribution of the scalar sum of the jet transverse momenta in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the invariant mass of the $b$-tagged jet and the highest-$p_{\mathrm{T}}$ forward jet in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of $p_{\mathrm{T}}$ of the highest-$p_{\mathrm{T}}$ forward jet in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the difference in $\eta$ between the highest-$p_{\mathrm{T}}$ forward jet and the photon in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the energy of the system formed by the highest-$p_{\mathrm{T}}$ forward jet and the photon in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions. The first and last bins include the underflow and overflow, respectively.

Distribution of the $\eta$ of the $b$-tagged jet in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions.

Distribution of the reconstructed top-quark mass in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions. The first and last bins include the underflow and overflow, respectively.

Distribution of the $p_{\mathrm{T}}$ of the top-quark and photon system in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the photon $p_{\mathrm{T}}$ in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions and the last bin includes the overflow.

Distribution of the photon $\eta$ in the $\geq$1fj$\,$SR in data and for the sum of all processes expectations before the profile-likelihood fit. The "Total" column corresponds to the sum of the expected contributions from the signal and background processes. The uncertainty represents the sum of statistical and systematic uncertainties in the signal and background predictions.

Ordered list of the 30 systematic uncertainties with the largest impact on the measured signal normalisation in the fit to data in the parton-level measurement considered as nuisance parameters (NPs) in the profile-likelihood fit. The column "NP value, error" corresponds to the nominal best-fit values and the corresponding uncertainties. The impact of each NP, $\Delta\sigma$/$\sigma_{\mathrm{pred}}$, is computed by comparing the nominal best-fit value of the POI ($\sigma$/$\sigma_{\mathrm{pred}}$) with the result of the fits when fixing the considered NP to its best-fit value shifted by its pre-fit and post-fit uncertainties. The corresponding impacts are listed in the "POI impact prefit high/low" and "POI impact high/low" columns, respectively. The "MC stat." NPs represent the MC statistical uncertainty and they enter the likelihood with a Poisson term, while all the other NPs enter the likelihood via a Gaussian term.

Ordered list of the 30 systematic uncertainties with the largest impact on the measured signal normalisation in the fit to data in the particle-level measurement considered as nuisance parameters (NPs) in the profile-likelihood fit. The column "NP value, error" corresponds to the nominal best-fit values and the corresponding uncertainties. The impact of each NP, $\Delta\sigma$/$\sigma_{\mathrm{pred}}$, is computed by comparing the nominal best-fit value of the POI ($\sigma$/$\sigma_{\mathrm{pred}}$) with the result of the fits when fixing the considered NP to its best-fit value shifted by its pre-fit and post-fit uncertainties. The corresponding impacts are listed in the "POI impact prefit high/low" and "POI impact high/low" columns, respectively. The "MC stat." NPs represent the MC statistical uncertainty and they enter the likelihood with a Poisson term, while all the other NPs enter the likelihood via a Gaussian term.

This table lists the kinematic requirements on parton-level objects used to define of the fiducial phase space for the parton-level measurement. Frixione isolation ($\href{https://arxiv.org/abs/hep-ph/9801442}{\text{hep-ph/9801442}}$) with a chosen radius of $\Delta R = 0.2$ is applied to photons ($\gamma$). The measured fiducial parton-level cross section is $\sigma_{tq\gamma}\times\mathcal{B}(t\rightarrow l\nu b) = 688\pm 23(\text{stat.})^{+75}_{-71}(\text{syst.})\,$fb.

This table lists the kinematic requirements on particle-level objects used to define of the fiducial phase space for the particle-level measurement. The particle level objects are photons ($\gamma$) not from a hadron decay, neutrinos not from a hadron decay ($\nu$), prompt electrons and muons ($\ell$) "dressed" by adding close-by ($\Delta R < 0.1$) photons, and anti-$k_t$ $R = 0.4$ jets built from stable particles ($\tau > 30\,$ps) and tau leptons excluding neutrinos and prompt dressed muons. Jets are $b$-tagged ($b$-jet) using ghost-matched $b$-hadrons with $p_{\text{T}} > 5\,$GeV. Apart from the kinematic requirements, isolation and overlap removal criteria are applied. Jets within $\Delta R = 0.4$ of a photon are removed if the $p_{\text{T}}$ of charged particles within $\Delta R = 0.3$ of the photon is smaller than $10\,\%$ of its $p_{\text{T}}$. Jets within $\Delta R = 0.4$ of a lepton are removed. Events are removed where a photon is close ($\Delta R < 0.4$) to a lepton or a surviving jet. The measured fiducial particle-level cross section is $\sigma_{tq\gamma}\times\mathcal{B}(t\rightarrow l\nu b)+\sigma_{t(\rightarrow l\nu b\gamma)q} = 303\pm 9(\text{stat.})^{+33}_{-32}(\text{syst.})\,$fb.

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.

The dihadron correlated yield normalized per radian per unit of pseudorapidity as function of $\Delta\eta$ in d+Au collisions on the away side (|$\Delta\phi$ - $\pi$| < $\pi$/3). Shown is the high FTPC-Au activity data. Trigger and associated particles have 1 < $p_T$ < 3 GeV/c and |$\eta$| < 1.

The dihadron correlated yield normalized per radian per unit of pseudorapidity as function of $\Delta\eta$ in d+Au collisions on the away side (|$\Delta\phi$ - $\pi$| < $\pi$/3). Shown is the high-activity data after subtracting the unscaled low-activity data. Trigger and associated particles have 1 < $p_T$ < 3 GeV/c and |$\eta$| < 1.

The dihadron correlated yield normalized per radian per unit of pseudorapidity as function of $\Delta\eta$ in d+Au collisions on the away side (|$\Delta\phi$ - $\pi$| < $\pi$/3). Shown is the high-activity data after subtracting the unscaled low-activity data. Trigger and associated particles have 1 < $p_T$ < 3 GeV/c and |$\eta$| < 1.

The dihadron correlated yield normalized per radian per unit of pseudorapidity as function of $\Delta\eta$ in d+Au collisions on the near (|$\Delta\phi$| < $\pi$/3) side. Shown is the high-activity data after subtracting the unscaled low-activity data. Trigger and associated particles have 1 < $p_T$ < 3 GeV/c and |$\eta$| < 1.

The dihadron correlated yield normalized per radian per unit of pseudorapidity as function of $\Delta\eta$ in d+Au collisions on the near (|$\Delta\phi$| < $\pi$/3, solid circles) and away side (|$\Delta\phi$ - $\pi$| < $\pi$/3, open circles). Shown are the (a) low and (b) high FTPC-Au activity data, and the high-activity data after subtracting the (c) unscaled. Trigger and associated particles have 1 < $p_T$ < 3 GeV/c and |$\eta$| < 1.

(a) The near-side jetlike correlated yield obtained from Gaussian fit as in Fig. 1 as function of the uncorrected dN/d$\eta$ at midrapidity measured in the TPC. Two event selections are used: FTPC-Au multiplicity and ZDC-Au energy. The curve is the result from a HIJING calculation.

(a) The near-side jetlike correlated yield obtained from Gaussian fit as in Fig. 1 as function of the uncorrected dN/d$\eta$ at midrapidity measured in the TPC. Two event selections are used: FTPC-Au multiplicity and ZDC-Au energy. The curve is the result from a HIJING calculation.

$p_T^{(a)}$ dependence. The ratio of the correlated yields in high over low FTPC-Au multiplicity events as a function of $p_T^{(a)}$ $p_T^{(t)}$ where $p_T^{(a)}$ $p_T^{(t)}$ is fixed.

$p_T^{(a)}$ dependence. The ratio of the correlated yields in high over low FTPC-Au multiplicity events as a function of $p_T^{(a)}$ $p_T^{(t)}$ where $p_T^{(a)}$ $p_T^{(t)}$ is fixed.

(a) The dihadron correlated yield normalized per radian per unit of pseudorapidity as a function of $\Delta\phi$ in d+Au collisions at high (0-20%) FTPC-Au multiplicities. Trigger and associated particles are 1 < $p_T$ < 3 GeV/c within 0.5 < |$\Delta\eta$| < 0.7. ZYAM positions are indicated with arrows.

(a) The dihadron correlated yield normalized per radian per unit of pseudorapidity as a function of $\Delta\phi$ in d+Au collisions at low (40-100%) FTPC-Au multiplicities. Trigger and associated particles are 1 < $p_T$ < 3 GeV/c within 0.5 < |$\Delta\eta$| < 0.7. ZYAM positions are indicated with arrows.multiplicity versus $\Delta\phi$.

The raw associated yield at high FTPC-Au multiplicity minus the unscaled ZYAM-subtracted correlated yields at low FTPC-Au multiplicity versus $\Delta\phi$.

The raw associated yield at high FTPC-Au multiplicity minus the scaled ZYAM-subtracted correlated yields at low FTPC-Au multiplicity versus $\Delta\phi$.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of pseudorapidity for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of pseudorapidity for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of pseudorapidity for events with the number of charged particles >=6 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of pseudorapidity for events with the number of charged particles >=6 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of transverse momentum for events with the number of charged particles >=1 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 2360 GeV as a function of transverse momentum for events with the number of charged particles >=1 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of transverse momentum for events with the number of charged particles >=1 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of transverse momentum for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of transverse momentum for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of transverse momentum for events with the number of charged particles >=6 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of transverse momentum for events with the number of charged particles >=6 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 900 GeV for events with the number of charged particles >=1 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 2360 GeV for events with the number of charged particles >=1 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 7000 GeV for events with the number of charged particles >=1 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 900 GeV for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 7000 GeV for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 900 GeV for events with the number of charged particles >=6 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 7000 GeV for events with the number of charged particles >=6 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Average transverse momentum in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=1 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Average transverse momentum in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=1 having transverse momentum >500 MeV and absolute(pseudorapidity) <2.5.

Average transverse momentum in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Average transverse momentum in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=2 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of pseudorapidity for events with the number of charged particles >=20 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of pseudorapidity for events with the number of charged particles >=20 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of pseudorapidity for events with the number of charged particles >=1 having transverse momentum >2500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of pseudorapidity for events with the number of charged particles >=1 having transverse momentum >2500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of transverse momentum for events with the number of charged particles >=20 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of transverse momentum for events with the number of charged particles >=20 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of transverse momentum for events with the number of charged particles >=1 having transverse momentum >2500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicities in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of transverse momentum for events with the number of charged particles >=1 having transverse momentum >2500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 900 GeV for events with the number of charged particles >=20 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 7000 GeV for events with the number of charged particles >=20 having transverse momentum >100 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 900 GeV for events with the number of charged particles >=1 having transverse momentum >2500 MeV and absolute(pseudorapidity) <2.5.

Charged-particle multiplicity distributions in proton-proton collisions at a centre-of mass energy of 7000 GeV for events with the number of charged particles >=1 having transverse momentum >2500 MeV and absolute(pseudorapidity) <2.5.

Average transverse momentum in proton-proton collisions at a centre-of mass energy of 900 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=1 having transverse momentum >2500 MeV and absolute(pseudorapidity) <2.5.

Average transverse momentum in proton-proton collisions at a centre-of mass energy of 7000 GeV as a function of the number of charged particles in the event for events with the number of charged particles >=1 having transverse momentum >2500 MeV and absolute(pseudorapidity) <2.5.

The average charged-particle muliplicity per unit of rapidity for ETARAP=0 as a function of the centre-of-mass energy.

The average charged-particle muliplicity per unit of rapidity in the pseudorapidity region -2.5 to 2.5 for events with 2 or more charged particles as a function of the centre-of-mass energy.

Charged particle multiplicity distribution.

Average charged particle transverse momentum as a function of the number of charged particles in the event.

Observed normalised distribution of parameter R (see text of article for details).. Data read from plot in the preprint.

Observed normalised distribution of parameter ALPHA (see text of article for details).. Data read from plot in the preprint.

Distributions in ALPHA (see text of paper) simulated using the HERWIG Monte-Carlo for the 'ideal' calorimenter and for the 'cdf' detector.. Data read from plot in the preprint.

Distributions in R (see text of paper) simulated using the HERWIG Monte-Carlo for the 'ideal' calorimenter and for the 'cdf' detector.. Data read from plot in the preprint.

Distributions in pseudrapidity of the third jet simulated using the HERWIG Monte-Carlo for the 'ideal' calorimenter and for the 'cdf' detector.. Data read from plot in the preprint.

Differential pseudorapidity distribution.. The numbers here at 630 GeV have been read from the plot in the paper.