Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a–c) SRs, (d–f) W+jets CRs and (g–i) tt̄ CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt̄ VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Expected (background-only) and observed 95% CL upper limits on the VLQ coupling κ as functions of the VLQ mass m_Q, for T-singlet (a) and Y-triplet (b) vector-like quarks. The green and yellow bands indicate the systematic uncertainties which are profiled in the fit for the observed upper limits. For the T-singlet model, the result of the latest ATLAS combination of searches for single vector-like top-quarks  is overlaid; this includes the decay modes T→H t and T→Z t. These interpretations are limited to parameter combinations for which the model corrections are valid, by restricting the VLQ relative width to Γ_Q/m_Q < 0.5, as indicated by the grey dashed line.

Expected (background-only) and observed 95% CL upper limits on the VLQ coupling κ as functions of the VLQ mass m_Q, for T-singlet (a) and Y-triplet (b) vector-like quarks. The green and yellow bands indicate the systematic uncertainties which are profiled in the fit for the observed upper limits. For the T-singlet model, the result of the latest ATLAS combination of searches for single vector-like top-quarks  is overlaid; this includes the decay modes T→H t and T→Z t. These interpretations are limited to parameter combinations for which the model corrections are valid, by restricting the VLQ relative width to Γ_Q/m_Q < 0.5, as indicated by the grey dashed line.

Exclusion limit (at 95% CL) for (a) T-singlet and (b) Y-triplet, expressed in terms of an upper limit on the VLQ relative width Γ_Q/m_Q, within its validity range for the modelling used, as a function of the VLQ mass m_Q. This is an alternative presentation of the upper limit set in terms of the coupling κ in Figure 6. As can be seen by the fact the interference-included and signal-only exclusion curves overlap almost entirely, the effect of neglecting the interference is negligible for the vector-like Y-quark.

Exclusion limit (at 95% CL) for (a) T-singlet and (b) Y-triplet, expressed in terms of an upper limit on the VLQ relative width Γ_Q/m_Q, within its validity range for the modelling used, as a function of the VLQ mass m_Q. This is an alternative presentation of the upper limit set in terms of the coupling κ in Figure 6. As can be seen by the fact the interference-included and signal-only exclusion curves overlap almost entirely, the effect of neglecting the interference is negligible for the vector-like Y-quark.

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.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $20 < p_{\textrm{T,jet}} < 30$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $30 < p_{\textrm{T,jet}} < 50$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $50 < p_{\textrm{T,jet}} < 100$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (without any WTA flavour requirement) to number of ungroomed jets (without any WTA flavour requirement). $10 < p_{\textrm{T,jet}} < 12$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (without any WTA flavour requirement) to number of ungroomed jets (without any WTA flavour requirement). $12 < p_{\textrm{T,jet}} < 15$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (without any WTA flavour requirement) to number of ungroomed jets (without any WTA flavour requirement). $15 < p_{\textrm{T,jet}} < 20$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (without any WTA flavour requirement) to number of ungroomed jets (without any WTA flavour requirement). $20 < p_{\textrm{T,jet}} < 30$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (without any WTA flavour requirement) to number of ungroomed jets (without any WTA flavour requirement). $30 < p_{\textrm{T,jet}} < 50$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (without any WTA flavour requirement) to number of ungroomed jets (without any WTA flavour requirement). $50 < p_{\textrm{T,jet}} < 100$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $10 < p_{\textrm{T,jet}} < 12$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $12 < p_{\textrm{T,jet}} < 15$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $15 < p_{\textrm{T,jet}} < 20$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $20 < p_{\textrm{T,jet}} < 30$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $30 < p_{\textrm{T,jet}} < 50$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data, without any WTA flavour requirement. Normalization is set to unity. $50 < p_{\textrm{T,jet}} < 100$ GeV.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (groomed) jets tagged with WTA flavour to number of (groomed) jets without WTA flavour tagging. $10 < p_{\textrm{T,jet}} < 12$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (groomed) jets tagged with WTA flavour to number of (groomed) jets without WTA flavour tagging. $12 < p_{\textrm{T,jet}} < 15$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (groomed) jets tagged with WTA flavour to number of (groomed) jets without WTA flavour tagging. $15 < p_{\textrm{T,jet}} < 20$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (groomed) jets tagged with WTA flavour to number of (groomed) jets without WTA flavour tagging. $20 < p_{\textrm{T,jet}} < 30$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (groomed) jets tagged with WTA flavour to number of (groomed) jets without WTA flavour tagging. $30 < p_{\textrm{T,jet}} < 50$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (groomed) jets tagged with WTA flavour to number of (groomed) jets without WTA flavour tagging. $50 < p_{\textrm{T,jet}} < 100$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (tagged with WTA flavour) to number of ungroomed jets (tagged with WTA flavour). $10 < p_{\textrm{T,jet}} < 12$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (tagged with WTA flavour) to number of ungroomed jets (tagged with WTA flavour). $12 < p_{\textrm{T,jet}} < 15$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (tagged with WTA flavour) to number of ungroomed jets (tagged with WTA flavour). $15 < p_{\textrm{T,jet}} < 20$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (tagged with WTA flavour) to number of ungroomed jets (tagged with WTA flavour). $20 < p_{\textrm{T,jet}} < 30$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (tagged with WTA flavour) to number of ungroomed jets (tagged with WTA flavour). $30 < p_{\textrm{T,jet}} < 50$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Groomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet,gr}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the soft drop tagging fraction, given by the number of groomed jets (tagged with WTA flavour) to number of ungroomed jets (tagged with WTA flavour). $50 < p_{\textrm{T,jet}} < 100$ GeV, soft drop $z_{\textrm{cut}}=0.1, \beta=0$.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (ungroomed) jets tagged with WTA flavour to number of (ungroomed) jets without WTA flavour tagging. $10 < p_{\textrm{T,jet}} < 12$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (ungroomed) jets tagged with WTA flavour to number of (ungroomed) jets without WTA flavour tagging. $12 < p_{\textrm{T,jet}} < 15$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (ungroomed) jets tagged with WTA flavour to number of (ungroomed) jets without WTA flavour tagging. $15 < p_{\textrm{T,jet}} < 20$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (ungroomed) jets tagged with WTA flavour to number of (ungroomed) jets without WTA flavour tagging. $20 < p_{\textrm{T,jet}} < 30$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (ungroomed) jets tagged with WTA flavour to number of (ungroomed) jets without WTA flavour tagging. $30 < p_{\textrm{T,jet}} < 50$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to the WTA tagging fraction, given by the number of (ungroomed) jets tagged with WTA flavour to number of (ungroomed) jets without WTA flavour tagging. $50 < p_{\textrm{T,jet}} < 100$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to unity. $10 < p_{\textrm{T,jet}} < 12$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to unity. $12 < p_{\textrm{T,jet}} < 15$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to unity. $15 < p_{\textrm{T,jet}} < 20$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to unity. $20 < p_{\textrm{T,jet}} < 30$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to unity. $30 < p_{\textrm{T,jet}} < 50$ GeV.

Ungroomed $B^\pm$-tagged jet invariant mass $m_{\textrm{jet}}/p_{\textrm{T,jet}}$ for $R=0.5$ jets reconstructed in pp data tagged with WTA flavour. Normalization is set to unity. $50 < p_{\textrm{T,jet}} < 100$ GeV.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{lead.\,lep.}}$.

Fiducial differential cross-sections as a function of $p_{\mathrm{T}}^{\mathrm{sub-lead.\,lep.}}$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.\,lep.}}$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T}}^{\mathrm{sub-lead.\,lep.}}$.

Fiducial differential cross-sections as a function of $p_{\mathrm{T},e\mu}$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$.

Fiducial differential cross-sections as a function of $|y_{e\mu}|$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $|y_{e\mu}|$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $|y_{e\mu}|$.

Fiducial differential cross-sections as a function of $m_{e\mu}$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $m_{e\mu}$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $m_{e\mu}$.

Fiducial differential cross-sections as a function of $|\Delta\phi_{e\mu}|$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $|\Delta\phi_{e\mu}|$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $|\Delta\phi_{e\mu}|$.

Fiducial differential cross-sections as a function of $\cos(\theta^{*})$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $\cos(\theta^{*})$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $\cos(\theta^{*})$.

Fiducial differential cross-sections as a function of $E_{\mathrm{T}}^{\mathrm{miss}}$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $E_{\mathrm{T}}^{\mathrm{miss}}$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $E_{\mathrm{T}}^{\mathrm{miss}}$.

Fiducial differential cross-sections as a function of $H_{\mathrm{T}}^{\mathrm{lep}+\mathrm{MET}}$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $H_{\mathrm{T}}^{\mathrm{lep}+\mathrm{MET}}$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $H_{\mathrm{T}}^{\mathrm{lep}+\mathrm{MET}}$.

Fiducial differential cross-sections as a function of $m_{\mathrm{T},e\mu}$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $m_{\mathrm{T},e\mu}$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $m_{\mathrm{T},e\mu}$.

Fiducial differential cross-sections as a function of the number of jets. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable Number of jets ($p_{\mathrm{T}} > $ 30 GeV).

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable Number of jets ($p_{\mathrm{T}} > $ 30 GeV).

Fiducial differential cross-sections as a function of $S_{\mathrm{T}}$. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The results are compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1, with the NNLO + PS predictions from Powheg MiNNLO + Pythia8 and Geneva + Pythia8, as well as Sherpa2.2.12 NLO + PS predictions. The last three predictions are combined with Sherpa 2.2.2 for the $gg$ initial state and Sherpa 2.2.12 for electroweak $WWjj$ production. These contributions are modelled at LO but a NLO QCD $k$-factor of 1.7 is applied for gluon induced production. Theoretical predictions are indicated as markers with vertical lines denoting PDF, scale and parton shower uncertainties. Markers are staggered for better visibility.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $S_{\mathrm{T}}$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $S_{\mathrm{T}}$.

Fiducial differential cross-sections for $p_{\mathrm{T},e\mu}$ in the region with 85 $<m_{e\mu}<$ 150, for the nNNLO MATRIX prediction and various four flavour PDF sets. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The measurement is compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1 using four different four-flavour NNLO PDF sets: NNPDF3.0, NNPDF3.1, CT18, and MSHT20.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$ (85 $<m_{e\mu}<$ 150).

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$ (85 $<m_{e\mu}<$ 150).

Fiducial differential cross-sections for $|y_{e\mu}|$ in the region with 85 $<m_{e\mu}<$ 150, for the nNNLO MATRIX prediction and various four flavour PDF sets. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The measurement is compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1 using four different four-flavour NNLO PDF sets: NNPDF3.0, NNPDF3.1, CT18, and MSHT20.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $|y_{e\mu}|$ (85 $<m_{e\mu}<$ 150).

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $|y_{e\mu}|$ (85 $<m_{e\mu}<$ 150).

Fiducial differential cross-sections for $p_{\mathrm{T},e\mu}$ in the region with 150 $<m_{e\mu}<$ 250, for the nNNLO MATRIX prediction and various four flavour PDF sets. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The measurement is compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1 using four different four-flavour NNLO PDF sets: NNPDF3.0, NNPDF3.1, CT18, and MSHT20.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$ (150 $<m_{e\mu}<$ 250).

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$ (150 $<m_{e\mu}<$ 250).

Fiducial differential cross-sections for $|y_{e\mu}|$ in the region with 150 $<m_{e\mu}<$ 250, for the nNNLO MATRIX prediction and various four flavour PDF sets. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The measurement is compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1 using four different four-flavour NNLO PDF sets: NNPDF3.0, NNPDF3.1, CT18, and MSHT20.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $|y_{e\mu}|$ (150 $<m_{e\mu}<$ 250).

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $|y_{e\mu}|$ (150 $<m_{e\mu}<$ 250).

Fiducial differential cross-sections for $p_{\mathrm{T},e\mu}$ in the region with $m_{e\mu}>$ 250, for the nNNLO MATRIX prediction and various four flavour PDF sets. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The right-hand-side axis indicates the integrated cross-section of the rightmost bin. The measurement is compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1 using four different four-flavour NNLO PDF sets: NNPDF3.0, NNPDF3.1, CT18, and MSHT20.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$ ($m_{e\mu}>$ 250).

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $p_{\mathrm{T},e\mu}$ ($m_{e\mu}>$ 250).

Fiducial differential cross-sections for $|y_{e\mu}|$ in the region with $m_{e\mu}>$ 250, for the nNNLO MATRIX prediction and various four flavour PDF sets. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The measurement is compared to fixed-order nNNLO QCD + NLO EW predictions of Matrix 2.1 using four different four-flavour NNLO PDF sets: NNPDF3.0, NNPDF3.1, CT18, and MSHT20.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $|y_{e\mu}|$ ($m_{e\mu}>$ 250).

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $|y_{e\mu}|$ ($m_{e\mu}>$ 250).

Measured value of the charge asymmetry, $A_{C}$.

Charge asymmetry $A_C$ measured as a function of the absolute difference of the absolute lepton rapidities $||\eta_{l+}|-|\eta_{l-}||$. The measured values are shown as points, whose error bars represent the statistical uncertainty, while the solid bands indicate the size of the total uncertainty. In the bottom panel, the central value of the charge asymmetry divided by the total uncertainty of the $A_C$ measurement is shown. The measurement is compared with the NNLO+PS predictions from Powheg MiNNLO+Pythia8 with vertical lines denoting PDF uncertainties.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $A_{C}$ vs. $||\eta_{l+}|-|\eta_{l-}||$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $A_{C}$ vs. $||\eta_{l+}|-|\eta_{l-}||$.

Charge asymmetry $A_C$ measured as a function of the invariant mass of the dilepton system $m_{e\mu}$. The measured values are shown as points, whose error bars represent the statistical uncertainty, while the solid bands indicate the size of the total uncertainty. In the bottom panel, the central value of the charge asymmetry divided by the total uncertainty of the $A_C$ measurement is shown. The measurement is compared with the NNLO+PS predictions from Powheg MiNNLO+Pythia8 with vertical lines denoting PDF uncertainties.

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $A_{C}$ vs. $m_{e\mu}$.

Correlation matrix of the total uncertainties in the measured fiducial cross section for the observable $A_{C}$ vs. $m_{e\mu}$.

Measured value of the asymmetry in the CP-odd observable $O_{z}$, $A_{CP}$.

The 95% CL limits on the cross-section $\sigma(pp \rightarrow Z' \rightarrow \chi \chi$) times branching ratio B in fb with all statistical and systematic uncertainties, for the $R_{\text{inv}}=$0.6 signal points.

Data yield in the PFN signal region.

Yield of data in the ANTELOPE signal region in the binning used for BumpHunter fitting.

Observed correlations between the production cross-sections multiplied by the $H \to WW^{\ast}$ branching ratio measured in data for each of the STXS categories.

Values of the negative log likelihood of 2-dimensional $\sigma_{VBF} \times B{H \to WW^{\ast}}$ versus $\sigma_{ggF} \times B_{H \to WW^{\ast}}$ scans.

Observed correlations between the production cross-sections multiplied by the $H \to WW^{\ast}$ branching ratio measured in data for each of the $\textrm{STXS}_\textrm{CP}$ categories.

Expected correlations between the production cross-sections multiplied by the $H \to WW^{\ast}$ branching ratio for each of the $\textrm{STXS}_\textrm{CP}$ categories.

Expected values and uncertainties for the signal strengths measured in each of the $\textrm{STXS}_\textrm{CP}$ categories, normalised to the corresponding SM predictions, for ggH and EW qqH production.

Best-fit values and uncertainties for the signal strengths measured in each of the $\textrm{STXS}_\textrm{CP}$ categories, normalised to the corresponding SM predictions, for ggH and EW qqH production.

The composition of the (orthonormal) eigenvectors defining the rotated basis in terms of operators in the Warsaw basis, labelled in terms of their corresponding Wilson coefficients.

Post-fit DNN distribution for the ggF different flavour 0-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 10\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 0-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 10\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 1-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 60\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 1-jet $60 \leq p_{T}^{\ell\ell, \textrm{miss}} < 120\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 1-jet $120 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 1-jet $200 \leq p_{T}^{\ell\ell, \textrm{miss}} < 300\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 1-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 300\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 2-jet $200 \leq p_{T}^{\ell\ell, \textrm{miss}} < 300\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 300\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF same flavour 2-jet $200 \leq p_{T}^{\ell\ell, \textrm{miss}} < 300\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 300\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 1-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 60\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 1-jet $60 \leq p_{T}^{\ell\ell, \textrm{miss}} < 120\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 1-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 120\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $700 \leq m_{jj} < 1000\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $1000 \leq m_{jj} < 1500\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 1500\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $700 \leq m_{jj} < 1000\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $1000 \leq m_{jj} < 1500\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 1500\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $350 \leq m_{jj} < 1000\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $1000 \leq m_{jj} < 1500\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 1500\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $350 \leq m_{jj} < 1000\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $1000 \leq m_{jj} < 1500\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the VBF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 1500\ \textrm{GeV}$ signal region; the post-fit results are obtained from the 15-POI fit.

Post-fit DNN distribution for the ggF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the ggF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $350 \leq m_{jj} < 700\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 700\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 700\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 700\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 700\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 700\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 700\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 700\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $0 \leq p_{T}^{\ell\ell, \textrm{miss}} < 200\ \textrm{GeV}$, $m_{jj} \geq 700\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 350\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 350\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 350\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF different flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 350\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 350\ \textrm{GeV}$, $-\pi \leq \Delta \phi_{jj}^\pm < - \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 350\ \textrm{GeV}$, $- \frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < 0$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 350\ \textrm{GeV}$, $0 \leq \Delta \phi_{jj}^\pm < \frac{\pi}{2}$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

Post-fit DNN distribution for the VBF same flavour 2-jet $p_{T}^{\ell\ell, \textrm{miss}} \geq 200\ \textrm{GeV}$, $m_{jj} \geq 350\ \textrm{GeV}$, $\frac{\pi}{2} \leq \Delta \phi_{jj}^\pm < \pi$ signal region used in the $\textrm{STXS}_\textrm{CP}$ measurement.

The extracted kinetic decoupling temperature is derived from $\pi^{-}$–d correlation functions.

This is the mixed event distribution for $\pi^{+}$–d, required for the fitting

This is the mixed event distribution for $\pi^{-}$–d, required for the fitting

The momentum smearing matrix, required for the fitting, used both for $\pi^{-}$–d and $\pi^{+}$–d

Data from Figure 2, panel d, upper panel, $v_{0}(p_{T})$ for $\eta_{gap}$ = 1, 0-5% Centrality

Data from Figure 2, panel d, lower panel, $v_{0}(p_{T})$ for $\eta_{gap}$ = 1, 50-60% Centrality

Data from Figure 3, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 0-5% Centrality, $\eta_{gap}$=0

Data from Figure 3, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 0-5% Centrality, $\eta_{gap}$=1

Data from Figure 3, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 0-5% Centrality, $\eta_{gap}$=2

Data from Figure 3, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 0-5% Centrality, $\eta_{gap}$=3

Data from Figure 3, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 60-70% Centrality, $\eta_{gap}$=0

Data from Figure 3, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 60-70% Centrality, $\eta_{gap}$=1

Data from Figure 3, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 60-70% Centrality, $\eta_{gap}$=2

Data from Figure 3, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, 60-70% Centrality, $\eta_{gap}$=3

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 0-5% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 5-10% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 10-20% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 20-30% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 30-40% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 40-50% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 50-60% Centrality

Data from Figure 4, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 60-70% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 0-5% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 5-10% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 10-20% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 20-30% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 30-40% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 40-50% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 50-60% Centrality

Data from Figure 4, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 60-70% Centrality

Data from Figure 5, panel a, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 0-5% Centrality

Data from Figure 5, panel b, $v_{0}(p_{T})/v_{0}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$=1, 0-5% Centrality

Data from Appendix, Figure 6, panel a, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 0-5% Centrality

Data from Appendix, Figure 6, panel b, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 10-20% Centrality

Data from Appendix, Figure 6, panel c, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 30-40% Centrality

Data from Appendix, Figure 6, panel d, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 60-70% Centrality

Data from Appendix, Figure 7, Zero Crossing Point of $v_{0}(p_{T})$ for $\eta_{gap}$ = 1

Data from Appendix, Figure 7, $\left\langle [p_{T}]\right\rangle$ for $p_{T}$ range of 0.5-10 GeV, $\eta_{gap}$ = 1

Data from Appendix, Figure 8, panel a, Closure of Sum Rule 1 for $\eta_{gap}$ = 1

Data from Appendix, Figure 8, panel b, Closure of Sum Rule 2 for $\eta_{gap}$ = 1

Data from Appendix, Figure 9, panel a, $v_{0}$ vs $N_{ch}$ for $\eta_{gap}$ = 1

Data from Appendix, Figure 9, panel b, $v_{0} / v^{5\%}_{0}$ vs $N_{ch}$ for $\eta_{gap}$ = 1

Data from Appendix, Figure 10, panel a, $v_{0}\sqrt{N_{ch}}$ vs $N_{ch}$ for $\eta_{gap}$ = 1

Data from Appendix, Figure 10, panel b, $v_{0}\sqrt{N_{ch}}$ vs Centrality for $\eta_{gap}$ = 1

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 0-5% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 5-10% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 10-20% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 20-30% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 30-40% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 40-50% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 50-60% Centrality

Data from Appendix, Figure 11, $v_{0}(p_{T})\,\sqrt{\left\langle N_{\mathrm{ch}}\right\rangle}$ for $p^{ref}_{T}$ = 0.5-2 GeV, $\eta_{gap}$ = 1, 60-70% Centrality

Data from Auxiliary, Figure 1, Top row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=0

Data from Auxiliary, Figure 1, Top row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=1

Data from Auxiliary, Figure 1, Top row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=2

Data from Auxiliary, Figure 1, Top row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=3

Data from Auxiliary, Figure 1, Top row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=0

Data from Auxiliary, Figure 1, Top row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=1

Data from Auxiliary, Figure 1, Top row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=2

Data from Auxiliary, Figure 1, Top row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=3

Data from Auxiliary, Figure 1, Bottom row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=0

Data from Auxiliary, Figure 1, Bottom row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=1

Data from Auxiliary, Figure 1, Bottom row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=2

Data from Auxiliary, Figure 1, Bottom row, Left column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 0-5% Centrality, $\eta_{gap}$=3

Data from Auxiliary, Figure 1, Bottom row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=0

Data from Auxiliary, Figure 1, Bottom row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=1

Data from Auxiliary, Figure 1, Bottom row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=2

Data from Auxiliary, Figure 1, Bottom row, Right column, $v_{0}(p_{T})$ for $p^{ref}_{T}$ = 0.5-5 GeV, 60-70% Centrality, $\eta_{gap}$=3

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 0-5% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 5-10% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 10-20% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 20-30% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 30-40% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 40-50% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 50-60% Centrality

Data from Auxiliary, Figure 2, $v_{0}(p_{T})$ For $\eta_{gap}$ = 0, and 60-70% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 0-5% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 5-10% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 10-20% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 20-30% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 30-40% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 40-50% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 50-60% Centrality

Data from Auxiliary, Figure 3, $v_{0}(p_{T})$ For $\eta_{gap}$ = 1, and 60-70% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 0-5% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 5-10% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 10-20% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 20-30% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 30-40% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 40-50% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 50-60% Centrality

Data from Auxiliary, Figure 4, $v_{0}(p_{T})$ For $\eta_{gap}$ = 2, and 60-70% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 0-5% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 5-10% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 10-20% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 20-30% Centrality

Data from Auxiliary, Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 30-40% Centrality

Data from Auxiliary Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 40-50% Centrality

Data from Auxiliary Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 50-60% Centrality

Data from Auxiliary Figure 5, $v_{0}(p_{T})$ For $\eta_{gap}$ = 3, and 60-70% Centrality