Figure 8(left) of the article. Differential fiducial cross-section of the Z boson $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 8(right) of the article. Differential fiducial cross-section of the leading $b$-jet $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 9 of the article. Differential fiducial cross-section of the $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z \left( \rightarrow \ell\ell \right) + 1 b$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 10(left) of the article. Differential fiducial cross-section of the $\Delta \phi$ between the leading and sub-leading $b$-jets in events with $Z \left( \rightarrow \ell\ell \right) + 2 b$-jets. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 10(right) of the article. Differential fiducial cross-section of the invariant mass of the leading and sub-leading $b$-jets in events with $Z \left( \rightarrow \ell\ell \right) + 2 b$-jets. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 11(left) of the article. Differential fiducial cross-section of the Z boson $p_T$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 11(right) of the article. Differential fiducial cross-section of the leading $c$-jet $p_T$ in events with $Z \left( \rightarrow \ell\ell ight) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Figure 12 of the article. Differential fiducial cross-section of the leading $c$-jet $x_F$ in events with $Z \left( \rightarrow \ell\ell \right) + 1 c$-jet. The thin inner band corresponds to the statistical uncertainty of the data, and the outer band to statistical and systematic uncertainties of the data, added in quadrature.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 18 & 24) for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 4.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 19 & 25) for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 9.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 20 & 26) for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 5.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 21 & 27) for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 10.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling (see Tables 22 & 28) for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 11.

Correction factors from parton level done with flavour dressing algorithms as seen in Ref. [2] of the paper of the paper to hadron level with R 0.3 cone labelling (see Tables 23 & 29) for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties for what concerns the passage from hadron level flavour dressing to hadron level cone labelling, plus statistical uncertainty of Pythia8 simulations used for parton to hadron corrections. See Table 6.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level R 0.3 cone labelling for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 12.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 13.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 14.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 15.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 16.

Correction factors from hadron level done with flavour dressing algorithms as seen in Ref. [2] of the paper of the paper to hadron level with R 0.3 cone labelling for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include Sherpa 2.2.11 and MGaMC+Py8 FxFx uncertainties. See Table 17.

Correction factors from parton level to hadron level for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors statistical uncertainty of Pythia8 simulations. See Table 12.

Correction factors from parton level to hadron level for the differential cross-section of the Z boson $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 13.

Correction factors from parton level to hadron level for the differential cross-section of the leading $b$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 14.

Correction factors from parton level to hadron level for the differential cross-section of the leading $c$-jet $p_T$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 15.

Correction factors from parton level to hadron level for the differential cross-section of the leading $c$-jet $x_F$ in events with $Z (\rightarrow ll) \ge 1 $ c-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 16.

Correction factors from parton level to hadron level for the differential cross-section of $\Delta R$ between the $Z$ boson and the leading $b$-jet in events with $Z (\rightarrow ll) \ge 1 $ b-jets. Systematic errors include statistical uncertainty of Pythia8 simulations. See Table 17.

Post-fit distribution of the GNN score evaluated with $m_{H/A}$ = 400 GeV in the validation region in the 2LOS region with $\geq 8$ jets. These regions do not enter the fit. The post-fit background prediction is obtained using the post-fit nuisance parameters from the background-only fit in the control and signal regions.

Observed and expected 95% CL upper limits on the cross-section times branching ratio,$ \sigma(pp \rightarrow t\bar{t}H/A ) \times B(H/A \rightarrow t\bar{t})$, as a function of $m_{H/A}$, obtained using the 1L/2LOS final states.

Observed and expected 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario where both the scalar $H$ and pseudo-scalar $A$ contribute with $m_H = m_A$ is shown.

Observed and expected 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario with the contribution from scalar $H$ only is shown.

Observed and expected 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario with the contribution from pseudo-scalar $A$ only is shown.

Expected and observed 95% CL upper limits on the cross-section times branching ratio,$ \sigma(pp \rightarrow t\bar{t}H/A ) \times B(H/A \rightarrow t\bar{t})$, as a function of $m_{H/A}$, obtained from the combination of the 1L/2LOS and 2LSS/ML final states.

Expected and observed 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS and 2LSS/ML final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario where both the scalar $H$ and pseudo-scalar $A$ contribute with $m_H = m_A$ is shown.

Expected and observed 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS and 2LSS/ML final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario with the contribution from scalar $H$ only is shown.

Expected and observed 95% CL lower limits on tanβ as a function of the $m_{H/A}$ mass obtained using the 1L/2LOS and 2LSS/ML final states, assuming a Type-II 2HDM in the alignment limit. Values of tanβ below the observed limit are excluded. The scenario with the contribution from pseudo-scalar $A$ only is shown.

Expected and Observed 95% CL upper limits on the $ \sigma(pp \rightarrow t\bar{t})\times B(S_8\rightarrow t\bar{t})$ production cross-section as a function of $m_{S_8}$, obtained from the combination of the 1L/2LOS and 2LSS/ML final states. The expected limits from the individual 1L/2LOS and 2LSS/ML analyses are also shown.

Post-fit distribution of the GNN scores evaluated with $m_{H/A}$ = 700 GeV in the 1L region with $\geq 10$ jets and four $b$-tagged jets. The fit is performed under the background-only hypothesis.

Post-fit distribution of the GNN scores evaluated with $m_{H/A}$ = 700 GeV in the 2LOS region with $\geq 8$ jets and $\geq 4$ $b$-tagged jets. The fit is performed under the background-only hypothesis.

Post-fit distribution of the GNN scores evaluated with $m_{H/A}=$1000 GeV,in the 1L region with $\geq 10$ jets four $b$-tagged jets. The fit is performed under the background-only hypothesis.

Post-fit distribution of the GNN scores evaluated with $m_{H/A}=$1000 GeV in the 2LOS region with $\geq 8$ jets and $\geq 4$ $b$-tagged jets. The fit is performed under the background-only hypothesis.

Post-fit distribution of the most important global features used in the GNN training, Sum of the pcb scores of the six jets with the highest scores ($\sum_{i\in[1,6]}\mathrm{pcb}_i$), in the region where the training is performed, i.e., the region with $\geq 9$ jets and $\geq3$ $b$-tagged jets in the 1L channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, Sum of the pcb scores of the six jets with the highest scores ($\sum_{i\in[1,6]}\mathrm{pcb}_i$), in the region where the training is performed i.e.the region with $\geq 7$ jets and $\geq3$ $b$-tagged jets in the 2LOS channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, $p_{\mathrm{T}}$ sum of all reconstructed leptons and jets ($\mathrm{H}_{\mathrm{T}}^{\mathrm{all}}$), in the region where the training is performed, i.e., the region with $\geq 9$ jets and $\geq3$ $b$-tagged jets in the 1L channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, $p_{\mathrm{T}}$ sum of all reconstructed leptons and jets ($\mathrm{H}_{\mathrm{T}}^{\mathrm{all}}$), in the region where the training is performed, i.e. the region with $\geq 7$ jets and $\geq3$ $b$-tagged jets in the 2LOS channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, Number of jets, in the region where the training is performed, i.e., the region with $\geq 9$ jets and $\geq3$ $b$-tagged jets in the 1L channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Post-fit distribution of the most important global features used in the GNN training, Number of jets, in the region where the training is performed, i.e., the region with $\geq 7$ jets and $\geq3$ $b$-tagged jets in the 2LOS channel. The fit is performed under the background-only hypothesis using the GNN scores evaluated with $m_{H/A} = 400$ GeV.

Dijet invariant mass distributions data compared to the fitted background estimates for the $j b b$ channel. The distributions are shown here with the $m_{jj}$ resolution binning.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma j j$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma b b$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $j j j$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma\times BR\times A$) as a function of the $Z^{`}$ mass for the $j b b$ channel with the background and signal contributions modeled by a 5-parameter fit function and templates from the signal samples, respectively.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma j j$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $\gamma b b$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) as a function of the $Z^{`}$ mass for the $j j j$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Expected and observed 95% CL upper limits on the product of the cross-section, acceptance and branching ratio ($\sigma\times BR\times A$) as a function of the $Z^{`}$ mass for the $j b b$ channel with the background and Gaussian distributed signal templates with a mass $m_{G}$ and width $\sigma_{G}$, respectively. Observed and expected limits corresponding to different choices of template widths (5, 7, 10, 12 and 15) are reported.

Total invariant mass distribution $m_{4\ell}$ in Signal Region 2.

Selected events in the ($m_{4\ell}$, $\left\langle m_{\ell\ell}\right\rangle$) plane, SR1

Selected events in the ($m_{4\ell}$, $\left\langle m_{\ell\ell}\right\rangle$) plane, SR2

Expected background events in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane, SR1

Expected signal shape for $(m_{S}, m_{Z_d}) = (110~\text{GeV},30~\text{GeV}$) in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane, SR1. The signal distributions are normalized to unit volume.

Expected background events in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane, SR2. Events that satisfy $|m_{ab,cd} - m_Z| < 8~\text{GeV}$ are removed by the Z-veto requirement, however not all events lying behind the mask in the plot, corresponding to $\langle m_{\ell\ell}\rangle$ values between 83 and 99 GeV, are deleted.

Expected signal shape for $(m_{S}, m_{Z_{d}}) = (750~\text{GeV},150~\text{GeV}$) in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane, SR2. The signal distributions are normalized to unit volume. Events that satisfy $|m_{ab,cd} - m_Z| < 8~\text{GeV}$ are removed by the Z-veto requirement, however not all events lying behind the mask in the plot, corresponding to $\langle m_{\ell\ell}\rangle$ values between 83 and 99 GeV, are deleted.

Expected 95% CL limit on $\sigma \mathcal{B}(S\rightarrow Z_{d}Z_{d} \rightarrow 4\ell)$, SR1

Observed 95% CL limit on $\sigma \mathcal{B}(S\rightarrow Z_{d}Z_{d} \rightarrow 4\ell)$, SR1

Observed / Expected 95% CL limit ratio in SR1

Expected 95% CL limit on $\mathcal{B}(S\rightarrow Z_{d}Z_{d} \rightarrow 4\ell)$ with Z-veto, SR2

Observed 95% CL limit on $\sigma \mathcal{B}(S\rightarrow Z_{d}Z_{d} \rightarrow 4\ell)$ with Z-veto, SR2

Observed / Expected 95% CL limit ratio with Z-veto, SR2

Local $p_0$-values in the ($m_{S}, m_{Z_{d}}$) plane, SR1

Local $p_0$-values in the ($m_{S}, m_{Z_{d}}$) plane, SR2 with Z-veto applied

Observed data in Signal Region 1 in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane.

Observed data in Signal Region 2 in the $(m_{4\ell}, \langle m_{\ell\ell}\rangle)$ plane. Events that satisfy $|m_{ab,cd} - m_Z| < 8~\text{GeV}$ are removed by the Z-veto requirement, however not all events lying behind the mask in the plot, corresponding to <m_ll> values between 83 and 99 GeV, are deleted.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 1 in the $4e$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 1 in the $2e2\mu$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 1 in the $4\mu$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 2 in the $4e$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 2 in the $2e2\mu$ final state.

Distributions of the average dilepton mass $\left\langle m_{\ell\ell}\right\rangle = \frac{1}{2}\left(m_{ab} + m_{cd}\right)$ for Signal Region 2 in the $4\mu$ final state.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 1 in the $4e$ final state. The SR1 requirement $m_{4\ell} < 115~\text{GeV}$ is not applied here.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 1 in the $2e2\mu$ final state. The SR1 requirement $m_{4\ell} < 115~\text{GeV}$ is not applied here.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 1 in the $4\mu$ final state. The SR1 requirement $m_{4\ell} < 115~\text{GeV}$ is not applied here.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 2 in the $4e$ final state.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 2 in the $2e2\mu$ final state.

Distributions of the total invariant mass $m_{4\ell}$ for Signal Region 2 in the $4\mu$ final state.

The observed 95&#37; CL exclusion limits as a function of &kappa;_2V (obtained using the signal strength &mu;_VBF as the POI) from the combined ggF and VBF signal regions, as shown by the solid black line. The value of &kappa;_&lambda; is fixed to 1. The blue and yellow bands show respectively the 1&sigma; and 2&sigma; bands around the expected exclusion limits, which are shown by the dashed black line. The expected exclusion limits are obtained using a fit to the data with the background-only hypothesis. The dark red line shows in the predicted VBF HH cross-section as a function of &kappa;_2V.

The observed 95&#37; CL exclusion limit obtained using the signal strength &mu;_ggF+VBF as the POI in the two-dimensional &kappa;_&lambda; vs. &kappa;_2V space, obtained from the combined ggF and VBF signal model, as shown by the solid black line. The blue and yellow bands show respectively the 1&sigma; and 2&sigma; bands around the expected exclusion limits, which are shown by the dashed black line. The star denotes the SM prediction (&kappa;_&lambda;=κ_2V=1). The displayed values are in units of $\mu_{ggF}$.

The expected and observed profile likelihood ratio scans for &kappa;_&lambda; coupling modifier, shown by the solid black line, using the coupling modifier as the POIs. The value of &kappa;_2V is fixed to 1. The dashed blue line shows the expected profile likelihood ratio, as obtained using a fit to the data with the background-only hypothesis. The pink line indicates the 2&sigma; exclusion boundary.

The expected and observed profile likelihood ratio scans for the &kappa;_2V coupling modifier, shown by the solid black line, using the coupling modifier as the POIs. The value of &kappa;_&lambda; is fixed to 1. The dashed blue line shows the expected profile likelihood ratio, as obtained using a fit to the data with the background-only hypothesis. The pink line indicates the 2&sigma; exclusion boundary.

The observed profile likelihood ratio exclusion limits for the two-dimensional &kappa;_&lambda; vs. &kappa;_2V modifier space, shown by the solid dark purple line at the 1&sigma; level and the dashed turquoise line at the 2&sigma; level. The black cross denotes the best-fit values of (&kappa;_&lambda;,&kappa;_2V). For both the expected and observed limit plots, the black star indicates the SM prediction (&kappa;_&lambda;=κ_2V=1).

The expected profile likelihood ratio exclusion limits for the two-dimensional &kappa;_&lambda; vs. &kappa;_2V modifier space, where the solid pink line denotes the 1&sigma;-level exclusion and the dashed orange line denotes the 2&sigma;-level exclusion. The black cross denotes the best-fit values of (&kappa;_&lambda;,&kappa;_2V). For both the expected and observed limit plots, the black star indicates the SM prediction (&kappa;_&lambda;=κ_2V=1).

The observed 95&#37; CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_{H G} vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95&#37; CL exclusion limits. The shaded blue band indicates the &plusmn; 1&sigma; uncertainty of the exclusion limits, while the yellow band indicates the &plusmn; 2&sigma; uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_tG vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95&#37; CL exclusion limits. The shaded blue band indicates the &plusmn; 1&sigma; uncertainty of the exclusion limits, while the yellow band indicates the &plusmn; 2&sigma; uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_{t H} vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95&#37; CL exclusion limits. The shaded blue band indicates the &plusmn; 1&sigma; uncertainty of the exclusion limits, while the yellow band indicates the &plusmn; 2&sigma; uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_H&#9633; vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95&#37; CL exclusion limits. The shaded blue band indicates the &plusmn; 1&sigma; uncertainty of the exclusion limits, while the yellow band indicates the &plusmn; 2&sigma; uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |&Delta;&eta;_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and &kappa;_&lambda;= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H&#9633;, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the two relevant HH HEFT coefficients: (a) c_tt&#772;HH and (b) c_ggHH, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95&#37; CL exclusion limits as a function of the two relevant HH HEFT coefficients: (a) c_tt&#772;HH and (b) c_ggHH, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1&sigma; and 2&sigma; bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

Observed and expected 95% CL upper limits on the visible $\tau\nu$ production cross section, $\sigma_{\rm vis} = \sigma(pp \to \tau\nu +X) \cdot \mathcal{A} \cdot \varepsilon$, for $m_{\rm T}$ thresholds ranging from 200 to 2950 GeV. See HepData abstract for details on how to use this data for reinterpretation.

Observed 95% CL upper limits on the cross-section times branching ratio as a function of the W′ mass in several NUGIM models defined by the value of the parameter cot$\theta$.

Expected 95% CL upper limits on the cross-section times branching ratio as a function of the W′ mass in several NUGIM models defined by the value of the parameter cot$\theta$.

Acceptance for $W^{\prime}_{\rm SSM}$ as a function of the $W^{\prime}_{\rm SSM}$ mass, shown after successively applying selection at generator-level. The acceptance times efficiency is calculated with respect to all $W^{\prime}_{\rm SSM} \to \tau\nu$ events with a generated $\tau\nu$ mass above 25 GeV. The "selected tau" criteria include the requirement of a $\tau_{\rm had-vis}$ with $p_{\rm T}$ > 30 GeV and $|\eta|$ < 2.4.

Acceptance times efficiency for $W^{\prime}_{\rm SSM}$ as a function of the $W^{\prime}_{\rm SSM}$ mass, shown after successively applying selection at reconstruction-level. The acceptance times efficiency is calculated with respect to all $W^{\prime}_{\rm SSM} \to \tau\nu$ events with a generated $\tau\nu$ mass above 25 GeV. "Preselection" includes all criteria prior to those shown.

Observed (points with error bars) and expected background (solid histograms) distributions for $E_{T}^{miss}$ in the signal region (a) SRL, (b) SRM and (c) SRH after the background-only fit applied to the CRs. The predicted signal distributions for the two models with a gluino mass of 2000 GeV and neutralino mass of 250 GeV (SRL), 1050 GeV (SRM) or 1950 GeV (SRH) are also shown for comparison. The uncertainties in the SM background are only statistical.

Observed and expected exclusion limit in the gluino-neutralino mass plane at 95% CL combined using the signal region with the best expected sensitivity at each point, for the full Run-2 dataset corresponding to an integrated luminosity of $139~\mathrm{fb}^{-1}$, for $\gamma/Z$ (a) and $\gamma/h$ (b) signal models. The black solid line corresponds to the expected limits at 95% CL, with the light (yellow) bands indicating the 1$\sigma$ exclusions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves, the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties. For each point in the higgsino-bino parameter space, the labels indicate the best-expected signal region, where L, M and H mean SRL, SRM and SRH, respectively.

Observed and expected exclusion limit in the gluino-neutralino mass plane at 95% CL combined using the signal region with the best expected sensitivity at each point, for the full Run-2 dataset corresponding to an integrated luminosity of $139~\mathrm{fb}^{-1}$, for $\gamma/Z$ (a) and $\gamma/h$ (b) signal models. The black solid line corresponds to the expected limits at 95% CL, with the light (yellow) bands indicating the 1$\sigma$ exclusions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves, the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties. For each point in the higgsino-bino parameter space, the labels indicate the best-expected signal region, where L, M and H mean SRL, SRM and SRH, respectively.

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Cutflow for the SRL selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 250 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Cutflow for the SRM selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 1050 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Cutflow for the SRH selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 1950 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $2\mu+\text{jets}$ region of the incluse jet phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $2e+\text{jets}$ region of the incluse jet phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the inclusive jet phase space for $1\mu+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the inclusive jet phase space for $1e+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the inclusive jet phase space for $2\mu+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the inclusive jet phase space for $2e+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{miss}$ differential cross-sections in the $p_\text{T}^\text{miss}+\text{jets}$ region of the VBF phase spaces, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $1\mu+\text{jets}$ region of the VBF phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $1e+\text{jets}$ region of the VBF phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $2\mu+\text{jets}$ region of the VBF phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $p_\text{T}^\text{recoil}$ differential cross-sections in the $2e+\text{jets}$ region of the VBF phase space, compared with the SM predictions. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the VBF phase space for $1\mu+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the VBF phase space for $1e+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the VBF phase space for $2\mu+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $p_\text{T}^\text{recoil}$ in the VBF phase space for $2e+\text{jets}$. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $p_\text{T}^\text{miss}+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $1\mu+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $1e+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $2\mu+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $m_{jj}$ distribution in the VBF phase space compared with the SM predictions, for the $2e+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $m_{jj}$ in the $1\mu+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $m_{jj}$ in the $e+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $m_{jj}$ in the $2\mu+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $m_{jj}$ in the $2e+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $p_\text{T}^\text{miss}+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $1\mu+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $1e+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $2\mu+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured $\Delta\phi_{jj}$ distributions in the VBF phase space compared with the SM predictions for the $2e+\text{jets}$ region. The middle panels show the ratios of the predictions to the data, along with their uncertainties, while the lower panels show the relative contributions from different SM processes relative to the total MEPS@NLO prediction. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $\Delta\phi_{jj}$ in the $1\mu+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $\Delta\phi_{jj}$ in the $1e+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $\Delta\phi_{jj}$ in the $2\mu+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

Comparison with SM predictions of the $\text{R}_\text{miss}$ ratios of the measured differential cross-sections for $\Delta\phi_{jj}$ in the $2e+\text{jets}$ region of the VBF phase space. The bottom panels show the ratios of the predictions to the data, along with their uncertainties. Note that individually numbered PDF components ('dK_PDF_') in the uncertainty breakdown correspond to NNPDF Hessian eigenvectors. Uncertainty components labeled 'VV_dK' include Vjj processes.

The measured Z &rarr; &nu;&nu; cross-section, differential in p_T^Z in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) &Delta;&phi;_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured Z &rarr; &nu;&nu; cross-section, differential in p_T^Z in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) &Delta;&phi;_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured Z &rarr; &nu;&nu; cross-section, differential in p_T^Z in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) &Delta;&phi;_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured Z &rarr; &nu;&nu; cross-section, differential in p_T^Z in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) &Delta;&phi;_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured p_T^Z, p_T^W and p_T^&gamma; differential cross-sections in the inclusive jet phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets,(e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^&gamma; differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets, (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured m_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets, (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured &Delta;&phi;_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets (b) &gamma;+jets (c) 2e+jets (d) 2&mu;+jets (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^&gamma; differential cross-sections in the inclusive jet phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets,(e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^&gamma; differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets, (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured m_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets, (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured &Delta;&phi;_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets (b) &gamma;+jets (c) 2e+jets (d) 2&mu;+jets (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^&gamma; differential cross-sections in the inclusive jet phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets,(e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^&gamma; differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets, (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured m_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets, (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured &Delta;&phi;_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets (b) &gamma;+jets (c) 2e+jets (d) 2&mu;+jets (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^&gamma; differential cross-sections in the inclusive jet phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets,(e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured p_T^Z, p_T^W and p_T^&gamma; differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets, (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured m_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets, (b) &gamma;+jets, (c) 2e+jets, (d) 2&mu;+jets, (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured &Delta;&phi;_jj differential cross-sections in the VBF phase spaces compared with the SM predictions: (a) p_T^miss+jets (b) &gamma;+jets (c) 2e+jets (d) 2&mu;+jets (e) e+jets and (f) &mu;+jets. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red shading).

The measured &gamma;+jets cross-section, differential in p_T^&gamma; in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) &Delta;&phi;_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured &gamma;+jets cross-section, differential in p_T^&gamma; in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) &Delta;&phi;_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured &gamma;+jets cross-section, differential in p_T^&gamma; in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) &Delta;&phi;_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

The measured &gamma;+jets cross-section, differential in p_T^&gamma; in the (a) single jet and (b) VBF phase spaces, and differential in (c) m_jj and (d) &Delta;&phi;_jj in the VBF phase space, compared with the SM predictions. The lower panels show the ratios of the predictions to the data, along with the data statistical uncertainties (black bars) and systematic uncertainties (red hatched band).

SM processes contributing to the fiducial SM predictions shown in Figure 7a

SM processes contributing to the fiducial SM predictions shown in Figure 7b

SM processes contributing to the fiducial SM predictions shown in Figure 8a

SM processes contributing to the fiducial SM predictions shown in Figure 8b

SM processes contributing to the fiducial SM predictions shown in Figure 8c

SM processes contributing to the fiducial SM predictions shown in Figure 8d

SM processes contributing to the fiducial SM predictions shown in Figure 9a

SM processes contributing to the fiducial SM predictions shown in Figure 9b

SM processes contributing to the fiducial SM predictions shown in Figure 9c

SM processes contributing to the fiducial SM predictions shown in Figure 9d

SM processes contributing to the fiducial SM predictions shown in Figure 3b of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 3c of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 3d of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 3e of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 5b of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 5c of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 5d of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 7b of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 7c of Auxiliary Material

SM processes contributing to the fiducial SM predictions shown in Figure 7d of Auxiliary Material

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a) and $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a) and $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a) and $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, $\geq 1$ jet phase space (Figure 7a) and $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c) and $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c) and $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c) and $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 8c) and $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a) and $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a) and $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a) and $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, $\geq 1$ jet phase space (Figure 8a) and $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d) and $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d) and $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d) and $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 8d) and $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b) and $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b) and $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b) and $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, $\geq 1$ jet phase space (Figure 8b) and $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 2c (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, $\geq 1$ jet phase space (Figure 2c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, $\geq 1$ jet phase space (Figure 10a) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, $\geq 1$ jet phase space (Figure 10a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 2d (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, $\geq 1$ jet phase space (Figure 2d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, $\geq 1$ jet phase space (Figure 10b) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, $\geq 1$ jet phase space (Figure 10b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b) and $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b) and $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 7b) and $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)) and $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)) and $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $1\mu$ + jets region, VBF phase space (Figure 3d (aux)) and $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)) and $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)) and $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 1e + jets region, VBF phase space (Figure 3b (aux)) and $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)) and $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)) and $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $2\mu$ + jets region, VBF phase space (Figure 3e (aux)) and $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)) and $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)) and $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, 2e + jets region, VBF phase space (Figure 3c (aux)) and $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 4c (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 4c (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 4a (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 4a (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 4d (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 4d (aux)).

Statistical correlations between $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 4b (aux)) and $p_\text{T}^\text{recoil}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 4b (aux)).

Statistical correlations between $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a) and $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a).

Statistical correlations between $m_{\text jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9a) and $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b).

Statistical correlations between $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)) and $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)).

Statistical correlations between $m_{\text jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 5d (aux)) and $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)).

Statistical correlations between $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)) and $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)).

Statistical correlations between $m_{\text jj}$ observable, 1e + jets region, VBF phase space (Figure 5b (aux)) and $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)).

Statistical correlations between $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c) and $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c).

Statistical correlations between $m_{\text jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9c) and $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d).

Statistical correlations between $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)) and $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)).

Statistical correlations between $m_{\text jj}$ observable, 2e + jets region, VBF phase space (Figure 5c (aux)) and $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)).

Statistical correlations between $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 6c (aux)) and $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 6c (aux)).

Statistical correlations between $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 6a (aux)) and $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 6a (aux)).

Statistical correlations between $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 10c) and $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 10c).

Statistical correlations between $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 6b (aux)) and $m_{\text jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 6b (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b) and $\Delta\phi_\text{jj}$ observable, $p_\text{T}^\text{miss}$ + jets region, VBF phase space (Figure 9b).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)) and $\Delta\phi_\text{jj}$ observable, $1\mu$ + jets region, VBF phase space (Figure 7d (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)) and $\Delta\phi_\text{jj}$ observable, 1e + jets region, VBF phase space (Figure 7b (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d) and $\Delta\phi_\text{jj}$ observable, $2\mu$ + jets region, VBF phase space (Figure 9d).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)) and $\Delta\phi_\text{jj}$ observable, 2e + jets region, VBF phase space (Figure 7c (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 8c (aux)) and $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $1\mu$ + jets region, VBF phase space (Figure 8c (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 8a (aux)) and $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 1e + jets region, VBF phase space (Figure 8a (aux)).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 10d) and $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ $2\mu$ + jets region, VBF phase space (Figure 10d).

Statistical correlations between $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 8b (aux)) and $\Delta\phi_\text{jj}$ observable, $\text{R}_\text{miss}$ observable, $R_{miss}$ 2e + jets region, VBF phase space (Figure 8b (aux)).

Observed event yields in $\textrm{SR}$ compared with post-fit expectations from Monte Carlo simulations, as a function of the scalar sum of lepton and jet transverse momenta, $H_{\mathrm{T}}$. The last bin includes overflow events. `Signal (prod.)' and `Signal (dec.)' refer to the single-top-quark production and top-quark pair decay signal contributions, respectively.

Expected and observed 95$\%$ CL upper limits on the branching ratio corresponding to the decay of a top quark to a muon and a tau lepton through a cLFV process for scalar, vector and tensor couplings. In the vector case, all vector operators are assumed to contribute simultaneously with the same effective coupling strength. The table shows the endpoint values of $\mathcal{B}(\mathrm{t}\to\mu\tau\mathrm{u})$ and $\mathcal{B}(\mathrm{t}\to\mu\tau\mathrm{c})$, which are interpolated linearly for each limit.

Expected and observed 95$\%$ CL upper limits on the Wilson coefficients for scalar, vector and tensor couplings of a top quark to a muon and a tau lepton through a cLFV process. In the vector case, all vector operators are assumed to contribute simultaneously with the same effective coupling strength. The table shows the endpoint values of the $|c^{\mu\tau\mathrm{ut}}|$ and $|c^{\mu\tau\mathrm{ct}}|$ Wilson coefficients, which are interpolated assuming a linear relationship between $\mathcal{B}(\mathrm{t}\to\mu\tau\mathrm{u})$ and $\mathcal{B}(\mathrm{t}\to\mu\tau\mathrm{c})$.

Observed event yields in $\textrm{SR}$ compared with pre-fit expectations from Monte Carlo simulations, as a function of the scalar sum of lepton and jet transverse momenta, $H_{\mathrm{T}}$. The last bin includes overflow events. The signal yields represent a leptoquark mass of $m_{S_{1}} = 1$ TeV and a coupling strength of $\lambda^{\textrm{LQ}} = 2.0$.

Observed event yields in $\textrm{SR}$ compared with post-fit expectations from Monte Carlo simulations, as a function of the scalar sum of lepton and jet transverse momenta, $H_{\mathrm{T}}$. The last bin includes overflow events. The signal yields represent a leptoquark mass of $m_{S_{1}} = 1$ TeV and a coupling strength of $\lambda^{\textrm{LQ}} = 2.0$.

Observed and expected exclusion 95$\%$ CL upper limits on the $S_{1}$ leptoquark coupling strength $\lambda^{\textrm{LQ}}$ as a function of LQ mass, $m_{S_{1}}$.

Theoretical cross-sections for single-top-quark production and top-quark decays through cLFV interactions involving vector, scalar and tensor EFT Wilson coefficients. The column titled as $c^{(ijk3)}_{\textrm{vector}}$ represents the individual cross-section contributions from each of $c_{\mathsf{lq}}^{-(ijk3)}$, $c_{\mathsf{eq}}^{(ijk3)}$, $c_{\mathsf{lu}}^{(ijk3)}$ and $c_{\mathsf{eu}}^{(ijk3)}$. The coefficient indices represent the lepton flavour generations ($i,j = 1,2,3$ where $i \neq j$) and light quark flavour generations ($k = 1,2$). The single-top-quark production cross-sections are quoted for $u$- and $c$-quark couplings separately, while they are combined for the $t\bar{t}$ decay process ($q_k = u,c$). The scale and PDF uncertainties are given. The value of each Wilson coefficient is set to 1.0 for the calculation of the cross-section.

Requirements for each analysis region. The symbol $\ell_{3}$ denotes the lowest $p_{\mathrm{T}}$ lepton. In $\mathrm{CR}t\bar{t}\mu$ an additional requirement is placed on the leading muon $p_{\mathrm{T}}$ ($p^{\mu_1}_\mathrm{T}$) and dilepton invariant masses in order to reject signal events.

Post-fit event yields for each analysis region entering the fit, with correlations on the full systematic uncertainties taken into account as determined in the fit under a signal+background hypothesis. The 'fake electron' category in the CR$t\bar{t}\mu$ control region collects small contributions primarily from $t\bar{t}\gamma$ and $Z\gamma$ which enter the event selection due to photon conversions.

Expected and observed 95$\%$ CL upper limits on Wilson coefficients corresponding to 2Q2L EFT operators which could introduce a cLFV top decay in the $\mu\tau$ channel, and existing limits from JHEP04 (2019) 014 (previous). The previous limits shown for $c^{1(ijk3)}_{\mathsf{lequ}}$ and $c^{3(ijk3)}_{\mathsf{lequ}}$ are tightened by a factor of $\sqrt{2}$, as JHEP04 (2019) 014 does not assume that these operators are Hermitian. Results are shown separately for the $\mu\tau ut$ and $\mu\tau ct$ interactions. The lepton generations are denoted by $i,j=2,3$ for $\mu$ and $\tau$ (where $i \neq j$) and the quark generations are denoted by $k=1,2$ for $u$ and $c$, respectively.

Expected and observed 95$\%$ CL upper limits on the branching ratio ($\mathcal{B}$) corresponding to the decay of a top quark to a muon and a $\tau$ lepton through a cLFV process using specific Wilson coefficients corresponding to 2Q2L EFT operators. Results are shown separately for $\mu\tau ut$ and $\mu\tau ct$ interactions. The lepton generations are denoted by $i,j=2,3$ for $\mu$ and $\tau$ (where $i \neq j$) and the quark generations are denoted by $k=1,2$ for $u$ and $c$, respectively.

Expected and observed 95% CL upper limits on the inclusive branching ratio ($\mathcal{B}$) corresponding to the decay of a top quark to a muon and a $\tau$-lepton through a cLFV process. Limits are shown for the statistical uncertainty only and for the full set of statistical and systematic uncertainties.

Expected and observed 95$\%$ CL upper limits on the Wilson coefficients corresponding to EFT operators inducing the decay of a top quark to a muon, a tau lepton and an up quark through a cLFV process ($\mu\tau ut$ interaction).

Expected and observed 95$\%$ CL upper limits on the Wilson coefficients corresponding to EFT operators inducing the decay of a top quark to a muon, a tau lepton and a charm quark through a cLFV process ($\mu\tau ct$ interaction).

Ranking of nuisance parameters by post-fit impact on cLFV signal strength $\mu$ (the parameter of interest, POI). The pre-fit (post-fit) impact of each nuisance parameter is calculated as the difference to the fitted value of cLFV signal strength between the nominal fit and a fit when fixing the corresponding nuisance parameter to $\hat{\theta}\pm\Delta\theta$ ($\hat{\theta}\pm\Delta\hat{\theta}$), where $\hat{\theta}$ is the best-fit value of the nuisance parameter and $\Delta\theta$ ($\Delta\hat{\theta}$) is its pre-fit (post-fit) uncertainty. The pulls of the nuisance parameters are calculated relative to zero. These pulls and their post-fit errors, $\Delta\hat{\theta} / \Delta\theta$, are given in the second column of the table.