The post-fit $m_{eff}$ distributions in the exclusion signal regions SRMM for the C1N2-WZ models. The uncertainty bands plotted include all statistical and systematic uncertainties. The dashed lines represent the benchmark signal samples. The overflow events, where present, are included in the last bin.

The post-fit $m_{eff}$ distributions in the exclusion signal regions SRHM for the C1C1-WW models. The uncertainty bands plotted include all statistical and systematic uncertainties. The dashed lines represent the benchmark signal samples. The overflow events, where present, are included in the last bin.

The post-fit $m_{eff}$ distributions in the exclusion signal regions SRHM for the C1N2-WZ models. The uncertainty bands plotted include all statistical and systematic uncertainties. The dashed lines represent the benchmark signal samples. The overflow events, where present, are included in the last bin.

Post-fit distributions in the SRs (not binned in signal output score) for representative kinematic observable $E_{T}^{miss}$. Two representative SUSY signal models are overlaid for illustration. The uncertainty bands plotted include all statistical and systematic uncertainties. The overflow events, where present, are included in the last bin.

Post-fit distributions in the SRs (not binned in signal output score) for representative kinematic observable $\sigma_{E_{T}^{miss}}$. Two representative SUSY signal models are overlaid for illustration. The uncertainty bands plotted include all statistical and systematic uncertainties. The overflow events, where present, are included in the last bin.

Post-fit distributions in the SRs (not binned in signal output score) for representative kinematic observable $am^{}_{T2}$. Two representative SUSY signal models are overlaid for illustration. The uncertainty bands plotted include all statistical and systematic uncertainties. The overflow events, where present, are included in the last bin.

Post-fit distributions in the SRs (not binned in signal output score) for representative kinematic observable $m_{PqbPaqb}$. Two representative SUSY signal models are overlaid for illustration. The uncertainty bands plotted include all statistical and systematic uncertainties. The overflow events, where present, are included in the last bin.

Post-fit distributions in the SRs (not binned in signal output score) for representative kinematic observable the signal output score. Two representative SUSY signal models are overlaid for illustration. The uncertainty bands plotted include all statistical and systematic uncertainties. The overflow events, where present, are included in the last bin.

Model-dependent exclusion contour at 95\% CL on the chargino pair production. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the chargino pair production. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the chargino pair production. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the chargino pair production. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the chargino pair production. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the chargino pair production. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the production of a chargino and a next-to-lightest neutralino. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the production of a chargino and a next-to-lightest neutralino. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the production of a chargino and a next-to-lightest neutralino. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the production of a chargino and a next-to-lightest neutralino. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the production of a chargino and a next-to-lightest neutralino. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95\% CL on the production of a chargino and a next-to-lightest neutralino. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band.

Model-dependent exclusion contour at 95 CL on the chargino and a next-to-lightest neutralino mass versus the mass difference between chargino and LSP in the C1N2-Wh model. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band. The observed and expected limit contours from the previous ATLAS analysis of the same data sample using standard cut-and-count methods are also shown for comparison.

Model-dependent exclusion contour at 95 CL on the chargino and a next-to-lightest neutralino mass versus the mass difference between chargino and LSP in the C1N2-Wh model. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band. The observed and expected limit contours from the previous ATLAS analysis of the same data sample using standard cut-and-count methods are also shown for comparison.

Model-dependent exclusion contour at 95 CL on the chargino and a next-to-lightest neutralino mass versus the mass difference between chargino and LSP in the C1N2-Wh model. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band. The observed and expected limit contours from the previous ATLAS analysis of the same data sample using standard cut-and-count methods are also shown for comparison.

Model-dependent exclusion contour at 95 CL on the chargino and a next-to-lightest neutralino mass versus the mass difference between chargino and LSP in the C1N2-Wh model. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band. The observed and expected limit contours from the previous ATLAS analysis of the same data sample using standard cut-and-count methods are also shown for comparison.

Model-dependent exclusion contour at 95 CL on the chargino and a next-to-lightest neutralino mass versus the mass difference between chargino and LSP in the C1N2-Wh model. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band. The observed and expected limit contours from the previous ATLAS analysis of the same data sample using standard cut-and-count methods are also shown for comparison.

Model-dependent exclusion contour at 95 CL on the chargino and a next-to-lightest neutralino mass versus the mass difference between chargino and LSP in the C1N2-Wh model. The observed limit is given by the solid line with the signal cross-section uncertainties shown by the dotted lines as indicated in the text. Expected limits are given by the dashed line with uncertainties shown by the shaded band. The observed and expected limit contours from the previous ATLAS analysis of the same data sample using standard cut-and-count methods are also shown for comparison.

Event selection cutflow for a representative signal sample for the C1C1-WW SRLM, including low meff exclusion SR, high meff exclusion SR and discovery SR. The masses of $\tilde{\chi}_{1}^{\pm}$ and $\tilde{\chi}_{1}^{0}$ are reported in the header. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Requirement for combination in the table stands for the number of combiBase lepton $<$ 2 and the number of combiBaseHighPt lepton $<$ 3.

Event selection cutflow for a representative signal sample for the C1C1-WW SRMM, including low meff exclusion SR, high meff exclusion SR and discovery SR. The masses of $\tilde{\chi}_{1}^{\pm}$ and $\tilde{\chi}_{1}^{0}$ are reported in the header. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Requirement for combination in the table stands for the number of combiBase lepton $<$ 2 and the number of combiBaseHighPt lepton $<$ 3.

Event selection cutflow for a representative signal sample for the C1C1-WW SRHM, including low meff exclusion SR, high meff exclusion SR and discovery SR. The masses of $\tilde{\chi}_{1}^{\pm}$ and $\tilde{\chi}_{1}^{0}$ are reported in the header. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Requirement for combination in the table stands for the number of combiBase lepton $<$ 2 and the number of combiBaseHighPt lepton $<$ 3.

Event selection cutflow for a representative signal sample for the C1N2-WZ SRLM, including low meff exclusion SR, high meff exclusion SR and discovery SR. The masses of $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ and $\tilde{\chi}_{1}^{0}$ are reported in the header. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Requirement for combination in the table stands for the number of combiBase lepton $<$ 2 and the number of combiBaseHighPt lepton $<$ 3.

Event selection cutflow for a representative signal sample for the C1N2-WZ SRMM, including low meff exclusion SR, high meff exclusion SR and discovery SR. The masses of $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ and $\tilde{\chi}_{1}^{0}$ are reported in the header. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Requirement for combination in the table stands for the number of combiBase lepton $<$ 2 and the number of combiBaseHighPt lepton $<$ 3.

Event selection cutflow for a representative signal sample for the C1N2-WZ SRHM, including low meff exclusion SR, high meff exclusion SR and discovery SR. The masses of $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ and $\tilde{\chi}_{1}^{0}$ are reported in the header. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Requirement for combination in the table stands for the number of combiBase lepton $<$ 2 and the number of combiBaseHighPt lepton $<$ 3.

Event selection cutflow for a representative signal sample for the C1N2-Wh SRXGB. The masses of $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ and $\tilde{\chi}_{1}^{0}$ are reported in the header. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown.

Event selection cutflow for a representative signal sample for the C1N2-Wh SRXGB. The masses of $\tilde{\chi}_{1}^{\pm}$/$\tilde{\chi}_{2}^{0}$ and $\tilde{\chi}_{1}^{0}$ are reported in the header. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown.

Observed cross-section upper limits on the chargino pair production.

Observed cross-section upper limits on the production of a chargino and a next-to-lightest neutralino.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the low $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the high $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the discovery SRs.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the low $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the high $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the discovery SRs.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the low $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the high $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WZ channel. The acceptance is shown as a function of the chargino mass for the discovery SRs.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the low $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the high $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the discovery SRs.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the low $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the high $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the discovery SRs.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the low $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the high $m_{eff}$ bins.

Fiducial acceptance of the SRs defined for the chargino-neutralino production in the WW channel. The acceptance is shown as a function of the chargino mass for the discovery SRs.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the low $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the high $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the discovery SRs.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the low $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the high $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the discovery SRs.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the low $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the high $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WZ channel. The efficiency is shown as a function of the chargino mass for the discovery SRs.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the low $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the high $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the discovery SRs.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the low $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the high $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the discovery SRs.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the low $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the high $m_{eff}$ bin.

Average detector and reconstruction efficiency of the SRs defined for the chargino-neutralino production in the WW channel. The efficiency is shown as a function of the chargino mass for the discovery SRs.

Observed cross-section upper limits on the production of a chargino and a next-to-lightest neutralino for the C1N2-Wh channel.

The chargino vs neutralino mass plane showing the acceptance for the Wh analysis at selection stage i.e., all cut applied but the one on the BDT score.

The chargino vs neutralino mass plane showing the detector and reconstruction efficiency of the SR before the selection on the BDT score.

The chargino vs neutralino mass plane showing the efficiency of the selection on the BDT score $w_{sig}$.

The invariant mass distribution of $B^+$ candidates is shown for $14 < p_{T} < 15$ GeV along with the corresponding fit.

The invariant mass distribution of $B^0$ candidates is shown for $15 < p_{T} < 16$ GeV along with the corresponding fit.

The invariant mass distribution of $B^0_{s}$ candidates is shown for $16 < p_{T} < 18$ GeV along with the corresponding fit.

The invariant mass distribution of $B^+$ candidates is shown for $13 < p_{T} < 18$ GeV along with the corresponding fit.

The invariant mass distribution of $B^0$ candidates is shown for $18 < p_{T} < 23$ GeV along with the corresponding fit.

The invariant mass distribution of $B^0_{s}$ candidates is shown for $23 < p_{T} < 28$ GeV along with the corresponding fit.

Measured $f_s/f_d$ as a function of $p_T$ using open-charm channels. Uncertainties include statistical and uncorrelated systematic components.

Measured $f_s/f_d$ as a function of $|y|$ using open-charm channels. Uncertainties include statistical and uncorrelated systematic components.

Measured $f_s/f_u$ as a function of $p_T$ using open-charm channels. Uncertainties include statistical and uncorrelated systematic components.

Measured $f_s/f_u$ as a function of $|y|$ using open-charm channels. Uncertainties include statistical and uncorrelated systematic components.

Measured $\mathcal{R}_{s}$ as a function of $p_{T}$ using charmonium channels, performed on the tag-side. The error bars include both statistical and bin-to-bin uncorrelated systematic uncertainties.

Measured $\mathcal{R}_{s}$ as a function of $p_{T}$ using charmonium channels, performed on the probe-side. The error bars include both statistical and bin-to-bin uncorrelated systematic uncertainties.

Measured $\mathcal{R}_{s}$ as a function of $|y|$ using charmonium channels, performed on the tag-side. The error bars include both statistical and bin-to-bin uncorrelated systematic uncertainties.

Measured $\mathcal{R}_{s}$ as a function of $|y|$ using charmonium channels, performed on the probe-side. The error bars include both statistical and bin-to-bin uncorrelated systematic uncertainties.

Measured $\mathcal{R}_{s}^{d}$ as a function of $p_{T}$ using charmonium channels, performed on the tag-side. The error bars include both statistical and bin-to-bin uncorrelated systematic uncertainties.

Measured $\mathcal{R}_{s}^{d}$ as a function of $|y|$ using charmonium channels, performed on the tag-side. The error bars include both statistical and bin-to-bin uncorrelated systematic uncertainties.

Measured $\mathcal{R}_{s}^{d}$ as a function of $|y|$ using charmonium channels, performed on the tag-side. The error bars include both statistical and bin-to-bin uncorrelated systematic uncertainties.

Measured $\mathcal{R}_{s}^{d}$ as a function of $p_{T}$ using charmonium channels, performed on the probe-side. The error bars include both statistical and bin-to-bin uncorrelated systematic uncertainties.

Measured $f_{d}/f_{u}$ as a function of $p_{T}$ using open-charm channels. The error bars include statistical and bin-to-bin uncorrelated systematic uncertainties. The global uncertainty on $f_{d}/f_{u}$ is 5.7$\%$ for the open-charm channel.

Measured $f_{d}/f_{u}$ as a function of $p_{T}$ using charmonium channels, performed on the tag-side. The error bars include statistical and bin-to-bin uncorrelated systematic uncertainties. The global uncertainty on $f_{d}/f_{u}$ is 4.8$\%$ for the charmonium channels for both the tag and probe sides.

Measured $f_{d}/f_{u}$ as a function of $p_{T}$ using charmonium channels, performed on the probe-side. The error bars include statistical and bin-to-bin uncorrelated systematic uncertainties. The global uncertainty on $f_{d}/f_{u}$ is 4.8$\%$ for the charmonium channels for both the tag and probe sides.

Measured $f_{d}/f_{u}$ as a function of $|y|$ using open-charm channels. The error bars include statistical and bin-to-bin uncorrelated systematic uncertainties. The global uncertainty on $f_{d}/f_{u}$ is 5.7$\%$ for the open-charm channel.

Measured $f_{d}/f_{u}$ as a function of $|y|$ using charmonium channels, performed on the tag-side. The error bars include statistical and bin-to-bin uncorrelated systematic uncertainties. The global uncertainty on $f_{d}/f_{u}$ is 4.8$\%$ for the charmonium channels for both the tag and probe sides.

Measured $f_{d}/f_{u}$ as a function of $|y|$ using charmonium channels, performed on the probe-side. The error bars include statistical and bin-to-bin uncorrelated systematic uncertainties.The global uncertainty on $f_{d}/f_{u}$ is 4.8$\%$ for the charmonium channels for both the tag and probe sides.

Measured $f_s/f_d$ as a function of $p_T$ using open-charm channels. Uncertainties include statistical and uncorrelated systematic components. The global uncertainties amount to 6.3$\%$ on $f_s/f_d$ for the open-charm channel

The $\mathcal{R}_{s}^{d}$ values from tag-side charmonium channels are converted to $f_s/f_d$ using the absolute normalization $c_{sd} = 1.64$. Uncertainties include statistical and uncorrelated systematic components. The global uncertainties amount to 8.4$\%$ on $f_s/f_d$ for both the charmonium channel.

Measured $f_s/f_u$ as a function of $p_T$ using open-charm channels. Uncertainties include statistical and uncorrelated systematic components. The global uncertainties amount to 7.4$\%$ on $f_s/f_d$ for the open-charm channel

The $\mathcal{R}_{s}$ values from tag-side charmonium channels are converted to $f_s/f_u$ using the absolute normalization $c_{su} = 2.02$. Uncertainties include statistical and uncorrelated systematic components. The global uncertainties amount to 8.7$\%$ on $f_s/f_d$ for both the charmonium channel.

The $\mathcal{R}_{s}$ values from the previous CMS measurement (PRL 131 (2023) 121901) are converted to $f_s/f_u$ using the absolute normalization $c_{su} = 2.02$. Uncertainties include statistical and uncorrelated systematic components. The global uncertainties amount to 8.7$\%$ on $f_s/f_d$ for both the previous CMS result.

$\sigma(\mathrm{pp}\to\mathrm{Z+Y(1S)})\mathcal{B}(\mathrm{Z}\to\mu\mu)\mathcal{B}(\mathrm{Y(1S)}\to\mu\mu) / \sigma(\mathrm{pp}\to\mathrm{Z})\mathcal{B}(\mathrm{Z}\to\mu\mu\mu\mu)$

$\sigma(\mathrm{pp}\to\mathrm{Z+Y(1S)})\mathcal{B}(\mathrm{Z}\to\mu\mu)\mathcal{B}(\mathrm{Y(1S)}\to\mu\mu) / \sigma(\mathrm{pp}\to\mathrm{Z})\mathcal{B}(\mathrm{Z}\to\mu\mu\mu\mu)$

$\sigma(\mathrm{pp}\to\mathrm{Z+Y(1S)})\mathcal{B}(\mathrm{Z}\to\mu\mu)\mathcal{B}(\mathrm{Y(1S)}\to\mu\mu) / \sigma(\mathrm{pp}\to\mathrm{Z})\mathcal{B}(\mathrm{Z}\to\mu\mu\mu\mu)$

inclusive DPS effective cross section

differential DPS effective cross section vs Y(1S) $p_\mathrm{T}$

differential DPS effective cross section vs Z $p_\mathrm{T}$

$\mathrm{J}/\psi\psi(2S)$ invariant-mass spectrum covering the full range of the fit: 6.76 - 15.0 GeV.

Squared masses (GeV^2) versus radial quantum number of (from left to right): the Upsilon family (triangles); the X(6600), X(6900), X(7100) states from Table 1 with statistical uncertainties only (circles); spin-1 diquark model predictions for 2++ states (line with green shading); spin-0 diquark model predictions for 0++ states (line with blue shading).

Masses and widths obtained from the Regge trajectory fit for the X states shown in Fig. 5 (statistical uncertainties only). The fit parameters are $\beta_0 = -9.374 \pm 0.355$ and $\beta = 0.241 \pm 0.008$, with fit quality $\chi^2/\mathrm{n_{bin}} = 119/113$ below 9 GeV, $\chi^2/\mathrm{n_{bin}} = 62/65$ below 7.8 GeV, where $\text{n}_{\text{bin}}$ is the number of nonzero bins.

Widths (in MeV) as a function of the radial quantum number. Shown are, from left to right: the total widths of the Upsilon family (solid squares); the three-gluon (3g) partial widths of the Upsilon family (open squares); and the widths of the X states (circles).

Comparison of the measured X Regge trajectory with theoretical predictions from spin-0 diquark model for 0++ states with L = 0.

Comparison of the measured X Regge trajectory with theoretical predictions from spin-1 diquark model for 0++ states with L = 0.

Comparison of the measured X Regge trajectory with theoretical predictions from spin-1 diquark model for 2++ states with L = 0.

Comparison of the measured X Regge trajectory with theoretical predictions from spin-1 diquark model for 0-+ states with L = 1.

Comparison of the measured X Regge trajectory with theoretical predictions from spin-1 diquark model for 1-+ states with L = 1.

Comparison of the measured X Regge trajectory with theoretical predictions from spin-1 diquark model for 2-+ states with L = 1.

Comparison of the measured X Regge trajectory with theoretical predictions from spin-0, 1 diquark model for (0, 1, 2)++ states with L = 2.

Differential cross section times branching fraction for Upsilon(2S) -> mu+ mu-, measured in the rapidity range 0.6 < |y| < 1.2. This table corresponds to Figure 2 (left panel for |y|<0.6, right panel for 0.6<|y|<1.2) and Table A.2 in the paper. Results assume unpolarized production; polarization correction factors are provided in Table 6.

Differential cross section times branching fraction for Upsilon(3S) -> mu+ mu-, measured in the rapidity range |y| < 0.6. This table corresponds to Figure 2 (left panel for |y|<0.6, right panel for 0.6<|y|<1.2) and Table A.3 in the paper. Results assume unpolarized production; polarization correction factors are provided in Table 6.

Differential cross section times branching fraction for Upsilon(3S) -> mu+ mu-, measured in the rapidity range 0.6 < |y| < 1.2. This table corresponds to Figure 2 (left panel for |y|<0.6, right panel for 0.6<|y|<1.2) and Table A.3 in the paper. Results assume unpolarized production; polarization correction factors are provided in Table 6.

Ratio of differential cross sections times branching fractions, Upsilon(2S)/Upsilon(1S), measured in the rapidity range |y| < 0.6. This table corresponds to the top-left panel of Figure 3 in the paper.

Ratio of differential cross sections times branching fractions, Upsilon(2S)/Upsilon(1S), measured in the rapidity range 0.6 < |y| < 1.2. This table corresponds to the top-right panel of Figure 3 in the paper.

Ratio of differential cross sections times branching fractions, Upsilon(3S)/Upsilon(1S), measured in the rapidity range |y| < 0.6. This table corresponds to the bottom-left panel of Figure 3 in the paper.

Ratio of differential cross sections times branching fractions, Upsilon(3S)/Upsilon(1S), measured in the rapidity range 0.6 < |y| < 1.2. This table corresponds to the bottom-right panel of Figure 3 in the paper.

Polarization correction factors for extreme polarization scenarios in the helicity frame (lambda_theta = +1 for transverse, lambda_theta = -1 for longitudinal). These factors allow conversion of the unpolarized cross sections (Tables 1-3) to any polarization scenario. This table corresponds to Table A.4 in the paper.

Covariance matrix of the absolute differential cross-section as function of $m^{bl}_{minimax}$ at particle level in the exclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $m^{bl}_{minimax}$ at particle level in the exclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $m^{bl}_{minimax}$ at particle level in the exclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $m^{bl}_{minimax}$ at particle level in the exclusive topology, accounting for the MC statistical uncertainties.

Total cross-section at particle level in the exclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text. The measured cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $\sigma_{WbWb}$ at particle level in the exclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $\sigma_{WbWb}$ at particle level in the exclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $N^{jets}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator. The last bin includes overflow events.

Covariance matrix of the absolute differential cross-section as function of $N^{jets}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $N^{jets}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $N^{jets}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator. The last bin includes overflow events.

Covariance matrix of the relative differential cross-section as function of $N^{jets}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $N^{jets}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $m^{bbll}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $m^{bbll}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $m^{bbll}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $m^{bbll}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $m^{bbll}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $m^{bbll}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $m_{T}^{bb4l}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $m_{T}^{bb4l}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $m_{T}^{bb4l}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $m_{T}^{bb4l}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $m_{T}^{bb4l}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $m_{T}^{bb4l}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $p_{T}^{b1b2}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{b1b2}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{b1b2}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $p_{T}^{b1b2}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{b1b2}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{b1b2}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $p_{T}^{jet1}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{jet1}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{jet1}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $p_{T}^{jet1}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{jet1}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{jet1}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $p_{T}^{jet2}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{jet2}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{jet2}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $p_{T}^{jet2}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{jet2}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{jet2}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $p_{T}^{lep1}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{lep1}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{lep1}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $p_{T}^{lep1}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{lep1}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{lep1}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $p_{T}^{lep2}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{lep2}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{lep2}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $p_{T}^{lep2}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{lep2}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{lep2}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $p_{T}^{bb4l}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{bb4l}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{bb4l}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $p_{T}^{bb4l}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{bb4l}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{bb4l}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Absolute differential cross-section as a function of $p_{T}^{bbll}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{bbll}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $p_{T}^{bbll}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Relative differential cross-section as a function of $p_{T}^{bbll}$ at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections. The covariance matrices are evaluated using pseudo-experiments for data and MC statistical uncertainties, and added to the individual covariance matrices for the remaining uncertainties, as described in the text. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{bbll}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the relative differential cross-section as function of $p_{T}^{bbll}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Total cross-section at particle level in the inclusive topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text. The measured cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.

Covariance matrix of the absolute differential cross-section as function of $\sigma_{WbWb}$ at particle level in the inclusive topology, accounting for the data statistical uncertainties.

Covariance matrix of the absolute differential cross-section as function of $\sigma_{WbWb}$ at particle level in the inclusive topology, accounting for the MC statistical uncertainties.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $m^{bl}_{minimax}$ in the exclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{jet2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{lep1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{lep2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $\sigma_{WbWb}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $p_{T}^{jet2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $p_{T}^{lep1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $p_{T}^{lep2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $p_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $p_{T}^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the exclusive topology and as function of $\sigma_{WbWb}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bbll}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bbll}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m^{bbll}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $p_{T}^{jet2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{lep1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $p_{T}^{lep2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $m_{T}^{bb4l}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{b1b2}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{jet2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $N^{jets}$ in the inclusive topology and as function of $p_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{bbll}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{bbll}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{bbll}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{bbll}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $p_{T}^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $p_{T}^{jet2}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $p_{T}^{lep1}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $p_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the absolute differential cross-sections at particle level as function of $\sigma_{WbWb}$ in the inclusive topology and as function of $p_{T}^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{jet2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{lep1}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{lep2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bl}_{minimax}$ in the exclusive topology and as function of $p_{T}^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bbll}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bbll}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m^{bbll}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $p_{T}^{jet2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{lep1}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $m_{T}^{bb4l}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{b1b2}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet2}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep1}$ in the inclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{jet1}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{lep2}$ in the inclusive topology and as function of $p_{T}^{jet2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $N^{jets}$ in the inclusive topology and as function of $p_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{bbll}$ in the inclusive topology and as function of $N^{jets}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{bbll}$ in the inclusive topology and as function of $m^{bbll}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{bbll}$ in the inclusive topology and as function of $m_{T}^{bb4l}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{bbll}$ in the inclusive topology and as function of $p_{T}^{b1b2}$ in the inclusive topology.

Statistical covariance matrix between the relative differential cross-sections at particle level as function of $p_{T}^{jet1}$ in the inclusive topology and as function of $p_{T}^{bbll}$ in the inclusive topology.

The measured differential cross-section for $W^+$ in bins of the $p_T$ of the $W$.

Statistical correlations of angular coefficients and differential cross-section for $W^-$ as a function of $p_{T}(W)$. Included as a CSV file due to file size.

Statistical correlations of angular coefficients and differential cross-section for $W^+$ as a function of $p_{T}(W)$. Included as a CSV file due to file size.

Ratio between the prompt $\Xi_c^+$ baryons in a multiplicity class to the multiplicity-integrated (INEL $>$ 0) class.

The prompt production yield ratios between $\Xi_c^0$ and $\rm {D}^0$ measured in the same multiplicity classes.

The prompt production yield ratios between $\Xi_c^+$ and $\rm {D}^0$ measured in the same multiplicity classes.

The prompt production yield ratios between $\Xi_c^0$ and $\Lambda_c^+$ measured in the same multiplicity classes.

The prompt production yield ratios between $\Xi_c^+$ and $\Lambda_c^+$ measured in the same multiplicity classes.

The branching-fraction ratio of BR($\Xi_c^0 \rightarrow \Xi^-e^+\nu_e$)/BR($\Xi_c^0 \rightarrow \Xi^-\pi^+$)

Relative systematic uncertainties in the averaged cross-section for various differential distributions in the (a, b) inclusive and (c, d) collinear phase spaces. The upper solid line shows the total uncertainty in the measured cross-section in data, and includes correlations between the systematic components. The &apos;Others&apos; category contains sub-dominant uncertainties arising from missing transverse momentum reconstruction and the jet-to-vertex fraction uncertainties.

Differential cross-section measurement in the inclusive phase space as function of (a) the minimum angular separation between the lepton and any jet with transverse momentum greater than 100&nbsp;GeV (&Delta;R _min(&#8467;,jet¹⁰⁰_i)), and (b) the ratio of W&nbsp;p_T to the closest-jet p_T (p_T^&#8467;&nu; / p_T^{closest jet}). The measured cross-sections in data and their statistical uncertainty are shown by the solid dots and error bars, while the shaded band shows the systematic and statistical uncertainties added in quadrature. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text. The bins around p_T^&#8467;&nu; / p_T^{closest jet} equal to 1 are merged to be insensitive to the singularity that exists in the fixed-order MCFM calculation. Additionally, in (b), the central two bins from the MCFM prediction are merged due to a singularity in fixed-order calculations, which requires p_T resummation of the W + jets system. The two lower panels show the effect of NLO EW_virt as computed from Sherpa 2.2.11.

Differential cross-section measurement in the inclusive phase space as function of (a) the minimum angular separation between the lepton and any jet with transverse momentum greater than 100&nbsp;GeV (&Delta;R _min(&#8467;,jet¹⁰⁰_i)), and (b) the ratio of W&nbsp;p_T to the closest-jet p_T (p_T^&#8467;&nu; / p_T^{closest jet}). The measured cross-sections in data and their statistical uncertainty are shown by the solid dots and error bars, while the shaded band shows the systematic and statistical uncertainties added in quadrature. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text. The bins around p_T^&#8467;&nu; / p_T^{closest jet} equal to 1 are merged to be insensitive to the singularity that exists in the fixed-order MCFM calculation. Additionally, in (b), the central two bins from the MCFM prediction are merged due to a singularity in fixed-order calculations, which requires p_T resummation of the W + jets system. The two lower panels show the effect of NLO EW_virt as computed from Sherpa 2.2.11.

Differential cross-section as a function of the invariant mass of the leading two jets (m_jj) in the inclusive, 2-jet phase space. The measured cross-sections in data and their statistical uncertainty are shown by the solid dots and error bars, while the shaded band shows the systematic and statistical uncertainties added in quadrature. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text. The two lower panels show the effect of NLO EW_virt as computed from Sherpa 2.2.11.

Differential distributions at reconstruction level in the (a, c) electron or (b, d) muon channel for (a, b) inclusive and (c, d) collinear signal regions after the application of the background normalisation factors. The signal process is stacked above all background predictions. The bottom panel shows the ratio of the data to the total signal plus background prediction. The shaded band includes statistical and systematic uncertainties from signal and background processes added in quadrature.

Differential distributions at reconstruction level in the (a, c) electron or (b, d) muon channel for (a, b) inclusive and (c, d) collinear signal regions after the application of the background normalisation factors. The signal process is stacked above all background predictions. The bottom panel shows the ratio of the data to the total signal plus background prediction. The shaded band includes statistical and systematic uncertainties from signal and background processes added in quadrature.

Relative systematic uncertainties in the averaged cross-section for various differential distributions in the (a, b) inclusive and (c, d) collinear phase spaces. The upper solid line shows the total uncertainty in the measured cross-section in data, and includes correlations between the systematic components. The &apos;Others&apos; category contains sub-dominant uncertainties arising from missing transverse momentum reconstruction and the jet-to-vertex fraction uncertainties.

Relative systematic uncertainties in the averaged cross-section for various differential distributions in the (a, b) inclusive and (c, d) collinear phase spaces. The upper solid line shows the total uncertainty in the measured cross-section in data, and includes correlations between the systematic components. The &apos;Others&apos; category contains sub-dominant uncertainties arising from missing transverse momentum reconstruction and the jet-to-vertex fraction uncertainties.

Differential cross-section as a function of (a) the leading p_T ^jet, (b) the inclusive jet multiplicity, (c) S_T, and (d) p^&#8467;&nu;_T in the collinear phase-space. The measured cross-sections in data and their statistical uncertainty are shown by the solid dots and error bars, while the shaded band shows the systematic and statistical uncertainties added in quadrature. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text. The two lower panels show the effect of NLO EW_virt as computed from Sherpa 2.2.11.

Differential cross-section as a function of (a) the leading p_T ^jet, (b) the inclusive jet multiplicity, (c) S_T, and (d) p^&#8467;&nu;_T in the collinear phase-space. The measured cross-sections in data and their statistical uncertainty are shown by the solid dots and error bars, while the shaded band shows the systematic and statistical uncertainties added in quadrature. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text. The two lower panels show the effect of NLO EW_virt as computed from Sherpa 2.2.11.

Differential cross-section as a function of (a) the leading p_T ^jet, (b) the inclusive jet multiplicity, (c) S_T, and (d) p^&#8467;&nu;_T in the collinear phase-space. The measured cross-sections in data and their statistical uncertainty are shown by the solid dots and error bars, while the shaded band shows the systematic and statistical uncertainties added in quadrature. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text. The two lower panels show the effect of NLO EW_virt as computed from Sherpa 2.2.11.

Differential cross-section as a function of (a) the leading p_T ^jet, (b) the inclusive jet multiplicity, (c) S_T, and (d) p^&#8467;&nu;_T in the collinear phase-space. The measured cross-sections in data and their statistical uncertainty are shown by the solid dots and error bars, while the shaded band shows the systematic and statistical uncertainties added in quadrature. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text. The two lower panels show the effect of NLO EW_virt as computed from Sherpa 2.2.11.

Measured fiducial cross-sections in each signal region. The measured cross-sections in data and their total uncertainty are shown by the solid dots and error band. Various theoretical predictions are overlaid and compared with the data in the lower panel. Uncertainties in predictions accurate to NLO precision include QCD scale and PDF uncertainties, while predictions that add NLO EW_virt (electroweak) corrections show uncertainties only due to different electroweak combination schemes. Uncertainties in predictions accurate at fixed-order NNLO precision are derived primarily from MCFM, do not include PDF variations, with an additional extrapolation from the Sherpa QCD with EW_virt prediction to assess the effects of higher-order electroweak corrections. On predictions with EW_virt corrections, the total uncertainty is shown as a shaded region, while the electroweak correction is overlaid with an error bar line.

Differential distributions at reconstructed-level comparing data with the background model in the (a) tt&#772; muon, (b) Z + jets muon, and (c) multijet electron control selections. All backgrounds and signal samples are stacked to produce the figures. The data points are shown as solid dots with their corresponding statistical uncertainty, while the total background model uncertainty is shown by the shaded band. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text.

Differential distributions at reconstructed-level comparing data with the background model in the multijet electron validation region for (a) the transverse momentum of the leading jet and (b) the transverse momentum of the lepton–neutrino system. All backgrounds and signal samples are stacked to produce the figures. The data points are shown as solid dots with their corresponding statistical uncertainty, while the total background model uncertainty is shown by the shaded band. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text.

Differential distributions at reconstructed-level comparing data with the background model in the multijet electron validation region for (a) the transverse momentum of the leading jet and (b) the transverse momentum of the lepton–neutrino system. All backgrounds and signal samples are stacked to produce the figures. The data points are shown as solid dots with their corresponding statistical uncertainty, while the total background model uncertainty is shown by the shaded band. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text.

Differential distributions at reconstructed-level comparing data with the background model in the (a) tt&#772; muon, (b) Z + jets muon, and (c) multijet electron control selections. All backgrounds and signal samples are stacked to produce the figures. The data points are shown as solid dots with their corresponding statistical uncertainty, while the total background model uncertainty is shown by the shaded band. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text.

Differential distributions at reconstructed-level comparing data with the background model in the (a) tt&#772; muon, (b) Z + jets muon, and (c) multijet electron control selections. All backgrounds and signal samples are stacked to produce the figures. The data points are shown as solid dots with their corresponding statistical uncertainty, while the total background model uncertainty is shown by the shaded band. Errors bars on the theory prediction include theoretical uncertainties as discussed in the text.

Response matrices from the Sherpa 2.2.11 event generator for (a) &Delta;R _min(&#8467;,jet¹⁰⁰_i) in the inclusive electron selection, and (b) transverse momentum of the leading jet in the collinear muon selection. The sum of the entries in each reconstructed bin (column) are normalised to unity.

Response matrices from the Sherpa 2.2.11 event generator for (a) &Delta;R _min(&#8467;,jet¹⁰⁰_i) in the inclusive electron selection, and (b) transverse momentum of the leading jet in the collinear muon selection. The sum of the entries in each reconstructed bin (column) are normalised to unity.

Signal strength and significance for $\text{t}\overline{\text{t}}\text{Z}(\text{Z}\to\text{b}\overline{\text{b}})$ decays with respect to the standard model expectation of unity.

Signal strength and significance for $\text{t}\overline{\text{t}}\text{Z}(\text{Z}\to\text{c}\overline{\text{c}})$ decays with respect to the standard model expectation of unity.

2D likelihood scans for $\kappa_{b}$ and $\kappa_{c}$ for $\text{t}\overline{\text{t}}\text{H}(\text{H}\to\text{c}\overline{\text{c}})$ and $\text{t}\overline{\text{t}}\text{H}(\text{H}\to\text{b}\overline{\text{b}})$.