Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) doublet representation assuming $\kappa$=0.5. The expected limits for the individual analyses are shown. The $HtZt$ analysis is only included in the limit calculation for $m_{\mathrm{T}}$ < 2.1 TeV.

Observed 95% CL exclusion limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of the universal coupling constant and the $T$-quark mass in the SU(2) singlet representation. Limits are only presented in the regime $\Gamma/m_{\mathrm{T}}$ < 50%, where the theory calculations are known to be valid.

Expected 95% CL exclusion limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of the universal coupling constant and the $T$-quark mass in the SU(2) singlet representation. Limits are only presented in the regime $\Gamma/m_{\mathrm{T}}$ < 50%, where the theory calculations are known to be valid.

Observed 95% CL exclusion limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of the universal coupling constant and the $T$-quark mass in the SU(2) doublet representation. Limits are only presented in the regime $\Gamma/m_{\mathrm{T}}$ < 50%, where the theory calculations are known to be valid.

Expected 95% CL exclusion limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of the universal coupling constant and the $T$-quark mass in the SU(2) doublet representation. Limits are only presented in the regime $\Gamma/m_{\mathrm{T}}$ < 50%, where the theory calculations are known to be valid.

Obs.Comb 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.Comb 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$+1\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$-1\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$+2\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$-2\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.OSML 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.HTZT 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.Monotop 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Obs.Comb 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.Comb 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$+1\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$-1\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$+2\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$-2\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.OSML 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.HTZT 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Observed upper limits at 95% CL on the $T$ quark mass as a function of the relative resonance width ($\Gamma_{\mathrm{T}}/m_{\mathrm{T}}$) and the relative coupling parameter $\xi_W$.

Expected upper limits at 95% CL on the $T$ quark mass as a function of the relative resonance width ($\Gamma_{\mathrm{T}}/m_{\mathrm{T}}$) and the relative coupling parameter $\xi_W$.

Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) singlet representation assuming $\kappa$=0.7. The expected limits for the individual analyses are shown.

Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) doublet representation assuming $\kappa$=0.7. The expected limits for the individual analyses are shown.

Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) singlet representation assuming $\kappa$=0.3. The observed limits for the individual analyses are shown. The $HtZt$ analysis is only included in the limit calculation for $m_{\mathrm{T}}$ < 2.1 TeV.

Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) singlet representation assuming $\kappa$=0.5. The observed limits for the individual analyses are shown. The $HtZt$ analysis is only included in the limit calculation for $m_{\mathrm{T}}$ < 2.1 TeV.

Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) singlet representation assuming $\kappa$=0.7. The observed limits for the individual analyses are shown.

Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) doublet representation assuming $\kappa$=0.3. The observed limits for the individual analyses are shown. The $HtZt$ analysis is only included in the limit calculation for $m_{\mathrm{T}}$ < 2.1 TeV.

Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) doublet representation assuming $\kappa$=0.5. The observed limits for the individual analyses are shown. The $HtZt$ analysis is only included in the limit calculation for $m_{\mathrm{T}}$ < 2.1 TeV.

Observed and expected 95% CL upper limits on the total cross-section σ($pp$ → $T$ → $Ht/Zt$) as a function of $T$-quark mass in the SU(2) doublet representation assuming $\kappa$=0.7. The observed limits for the individual analyses are shown.

Obs.Comb 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.Comb 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$+1\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$-1\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$+2\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$-2\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Obs.OSML 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Obs.HTZT 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Obs.Monotop 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) singlet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Obs.Comb 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.Comb 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$+1\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$-1\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$+2\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Exp.$-2\sigma$ for the 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Obs.OSML 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

Obs.HTZT 95% CL exclusion limits on the universal coupling constant $\kappa$ as a function of the $T$ quark mass in the SU(2) doublet representations for the combination. Limits are only presented in the regime $\Gamma_{\mathrm{T}}/m_{\mathrm{T}} < 50\%$, where the theory calculations are known to be valid.

The cutflow table for $N1$ and $N2$ signal samples for different mass points. The samples consist of both $ee$ and $\mu\mu$ final states in equal ratio. Each sample is normalized to a total cross-section of 0.1 pb. The abbreviations used in the table are, SC = Same Charge, SF = Same Flavor.

The cutflow table for $N3$ signal samples for different mass points. The samples consist of both $ee$ and $\mu\mu$ final states in equal ratio. Each sample is normalized to a total cross-section of 10 pb. The abbreviations used in the table are, SS = Same Charge, SF = Same Flavor.

Expected and observed upper limits on the strength of HNL mixing with electron neutrinos

Expected and observed upper limits on the strength of HNL mixing with muon neutrinos

Expected and observed upper limits on the strength of HNL mixing with tau neutrinos

Invariant mass $m_{J}$ of the resonance candidates in the region defined with forward photon $\eta_{\gamma} > 1.3$ and anti-tagged large-$R$ jet after the fit to data under the background-only hypothesis. The total systematic uncertainty is shown as the hatched band. Three representative $Z^{`}$ signal distributions are overlaid as red lines. The signal is shown for $g_q=0.2$ with production cross sections of 309 fb, 143 fb, and 34.2 fb for $m_{Z^{`}}=(20,~50,~\text{and}~125~\text{GeV}$), respectively.

Observed (expected) upper exclusion limits at 95$\%$ CL on the coupling strength $g_q$ of a new vector $Z^{`}$ particle decaying to a $q\bar q$ pair for a LHC DM WG benchmark signal model where decay rates for all decay modes except $Z^{`}\to q \bar q$ are set to 0, and the interference between the $Z^{`}$ and the SM $Z$ boson is neglected.

Observed (solid lines) and expected (dashed lines) upper exclusion limits at 95$\%$ CL on the product of the $Z^{`}$ production production cross section, branching ratio for $Z^{`}\to q\bar q$ and acceptance ($\sigma_{Z^{`}}\times B(Z^{`}\to q\bar q)\times A$) of a new $Z^{`}$ resonance for a LHC DM WG benchmark signal model where all decay modes except $Z^{`}\to q \bar q$ are set to 0, and the interference between the $Z^{`}$ and the SM $Z$ boson is neglected.

Distribution of the reconstructed $\tau\tau$ invariant mass ($m_{\tau\tau}$) for all events in the VBF_1 signal region for $p_{\text{T}}^{H}>200$ GeV. The observed Higgs boson signal corresponds to $(\sigma\times B)/(\sigma\times B)_{\text{SM}}\,=\,0.99$. Entries with values above the $x$-axis range are shown in the last bin of each distribution. The prediction for each sample is determined from the likelihood fit performed to measure the total $pp\rightarrow H\rightarrow\tau\tau$ cross-section.

Distribution of the reconstructed $\tau\tau$ invariant mass ($m_{\tau\tau}$) for all events in the VBF_0 signal region for $m_{jj}<1$ TeV. The observed Higgs boson signal corresponds to $(\sigma\times B)/(\sigma\times B)_{\text{SM}}\,=\,0.99$. Entries with values above the $x$-axis range are shown in the last bin of each distribution. The prediction for each sample is determined from the likelihood fit performed to measure the total $pp\rightarrow H\rightarrow \tau\tau$ cross-section.

Distribution of the reconstructed $\tau\tau$ invariant mass ($m_{\tau\tau}$) for all events in the VBF_0 signal region for $m_{jj}>1$ TeV. The observed Higgs boson signal corresponds to $(\sigma\times B)/(\sigma\times B)_{\text{SM}}\,=\,0.99$. Entries with values above the $x$-axis range are shown in the last bin of each distribution. The prediction for each sample is determined from the likelihood fit performed to measure the total $pp\rightarrow H \rightarrow \tau\tau$ cross-section.

Distribution of the reconstructed $\tau\tau$ invariant mass ($m_{\tau\tau}$) for all events in the VBF_1 signal region for $m_{jj}<1$ TeV. The observed Higgs boson signal corresponds to $(\sigma\times B)/(\sigma\times B)_{\text{SM}}\,=\,0.99$. Entries with values above the $x$-axis range are shown in the last bin of each distribution. The prediction for each sample is determined from the likelihood fit performed to measure the total $pp\rightarrow \rightarrow \tau\tau$ cross-section.

Distribution of the reconstructed $\tau\tau$ invariant mass ($m_{\tau\tau}$) for all events in the VBF_1 signal region for $m_{jj}>1$ TeV. The observed Higgs boson signal corresponds to $(\sigma\times B)/(\sigma\times B)_{\text{SM}}\,=\,0.99$. Entries with values above the $x$-axis range are shown in the last bin of each distribution. The prediction for each sample is determined from the likelihood fit performed to measure the total $pp\rightarrow H\rightarrow \tau\tau$ cross-section.

Comparison of the observed and expected event yields in the ttH categories. The window category contains events with $m_{\tau\tau} \in [100, 150]$ GeV, while events outside of this mass region are included in the sideband region. The observed Higgs boson signal corresponds to $(\sigma\times B)/(\sigma\times B)_{\text{SM}}\,=\,0.99$. The prediction for each sample is determined from the likelihood fit performed to measure the total $pp\rightarrow H\rightarrow\tau\tau$ cross-section.

The measured values of $\sigma_{H}\times B(H\rightarrow\tau\tau)$ relative to the SM expectations in the total (labelled as 'Combined') and per-production-mode fit. The total $\pm1\sigma$ uncertainty in each measurement is indicated by the error bar. The total systematic uncertainty in each measurement is also indicated.

The measured values for $\sigma_{H}\times B(H\rightarrow\tau\tau)$ relative to the SM expectations in the simplified template cross-section measurement. The total $\pm1\sigma$ uncertainty in each measurement is indicated by the error bar. The total systematic uncertainty in each measurement is also indicated.

Upper limits at 95% CL on simplified template cross-sections in the individual ttH $p_{\text{T}}^{H}$ bins relative to their SM expectation in the $H\rightarrow\tau\tau$ decay channel, derived using the CL$_\text{s}$ method. The observed upper limits, together with the expected limits both in the background-only hypothesis and in the SM hypothesis (dotted red lines) are included. In the case of the expected limits in the background-only hypothesis, one- and two-standard-deviation uncertainty bands are also shown.

Measured fiducial differential cross-sections as a function of $p_\text{T}(j_0)$. The measurements are compared with particle-level SM predictions from the POWHEG+PYTHIA8, POWHEG+HERWIG7 and MadGraph5_aMC@NLO+Pythia8 generators for the combined VBF, ggF, $VH$, and $t\bar{t}H$ Higgs boson production modes. The shaded box around each data point shows the statistical uncertainty, while the total uncertainty is indicated by the error bar. The contribution from the ggF, $VH$, and $t\bar{t}H$ production modes as predicted by POWHEG+PYTHIA8 is also shown.

Measured fiducial differential cross-sections as a function of $p_\text{T}^{H}$. The measurements are compared with particle-level SM predictions from the POWHEG+PYTHIA8, POWHEG+HERWIG7 and MadGraph5_aMC@NLO+Pythia8 generators for the combined VBF, ggF, $VH$, and $t\bar{t} H$ Higgs boson production modes. The shaded box around each data point shows the statistical uncertainty, while the total uncertainty is indicated by the error bar. The contribution from the ggF, $VH$, and $t\bar{t} H$ production modes as predicted by POWHEG+PYTHIA8 is also shown.

Measured fiducial differential cross-sections as a function of $\Delta\phi_{jj}^{\text{signed}}$. The measurements are compared with particle-level SM predictions from the POWHEG+PYTHIA8, POWHEG+HERWIG7 and MadGraph5_aMC@NLO+Pythia8 generators for the combined VBF, ggF, $VH$, and $t\bar{t}H$ Higgs boson production modes. The shaded box around each data point shows the statistical uncertainty, while the total uncertainty is indicated by the error bar. The contribution from the ggF, $VH$, and $t\bar{t} H$ production modes as predicted by POWHEG+PYTHIA8 is also shown.

Measured fiducial differential cross-sections as a function of $\Delta\phi_{jj}^{\text{signed}}$ vs $p_\text{T}^\text{H}$. The measurements are compared with particle-level SM predictions from the POWHEG+PYTHIA8, POWHEG+HERWIG7 and MadGraph5_aMC@NLO+Pythia8 generators for the combined VBF, ggF, $VH$, and $t\bar{t}H$ Higgs boson production modes. The shaded box around each data point shows the statistical uncertainty, while the total uncertainty is indicated by the error bar. The contribution from the ggF, $VH$, and $t\bar{t}H$ production modes as predicted by POWHEG+PYTHIA8 is also shown.

The unfolded data compared with the nominal SM predictions from POWHEG+PYTHIA8, as well as the predictions for several values of Wilson coefficients in the SMEFT, for $\Delta\phi_{jj}^{\text{signed}}$.

The unfolded data compared with the nominal SM predictions from POWHEG+PYTHIA8, as well as the predictions for several values of Wilson coefficients in the SMEFT, for $\Delta\phi_{jj}^{\text{signed}}$.

The unfolded data compared with the nominal SM predictions from POWHEG+PYTHIA8, as well as the predictions for several values of Wilson coefficients in the SMEFT, for $\Delta\phi_{jj}^{\text{signed}}$ vs $p_\text{T}^{H}$.

The unfolded data compared with the nominal SM predictions from POWHEG+PYTHIA8, as well as the predictions for several values of Wilson coefficients in the SMEFT, for $p_\text{T}^{H}$.

The 95% expected and observed confidence intervals for each of the six considered Wilson coefficients. Each coefficient is treated individually and both the linear and linear + quadratic models are considered when evaluating the sensitivity to the terms that enter at $\mathcal{O}(\Lambda^{-4})$. For the CP-even operators the $\Delta\phi_{jj}^{\text{signed}}$ distribution is used to extract the confidence interval. The limits are computed at a new-physics scale $\Lambda=1$ TeV.

The 95% expected and observed confidence intervals for each of the six considered Wilson coefficients. Each coefficient is treated individually and both the linear and linear + quadratic models are considered when evaluating the sensitivity to the terms that enter at $\mathcal{O}(\Lambda^{-4})$. For the CP-odd operators the $\Delta\phi_{jj}^{\text{signed}}$ vs $p_\text{T}^{H}$ distribution is used to extract the confidence interval. The limits are computed at a new-physics scale $\Lambda=1$ TeV.

Two-dimensional observed and expected limits at 68% and 95% confidence level obtained from $c_{HW}$ and $c_{HB}$ using only the SM-dimension-6 interference in the SMEFT basis. The limits are computed at a new-physics scale $\Lambda$ = 1 TeV. In this case, the $\Delta\phi_{jj}^{signed}$ distribution is used. IMPORTANT: HEPData requires the same number of rows for each column. However, each curve of on the plot has a different number of points. For that reason, the curves are padded with data of "0.0, 0.0". The "0.0, 0.0" points should be omitted in analysis.

Two-dimensional observed and expected limits at 68% and 95% confidence level obtained from $c_{H\tilde{W}}$ and $c_{H\tilde{B}}$ using only the SM-dimension-6 interference in the SMEFT basis. The limits are computed at a new-physics scale $\Lambda$ = 1 TeV. In this purely CP-odd case, the $\Delta\phi_{jj}^{signed}$ vs $p_{T}^{H}$ distribution is used. IMPORTANT: HEPData requires the same number of rows for each column. However, each curve of on the plot has a different number of points. For that reason, the curves are padded with data of "0.0, 0.0". The "0.0, 0.0" points should be omitted in analysis.

Two-dimensional observed and expected limits at 68% and 95% confidence level obtained from $c_{HW}$ and $c_{H\tilde{W}}$ using only the SM-dimension-6 interference in the SMEFT basis. The limits are computed at a new-physics scale $\Lambda$ = 1 TeV. In this case, the $\Delta\phi_{jj}^{signed}$ distribution is used. IMPORTANT: HEPData requires the same number of rows for each column. However, each curve of on the plot has a different number of points. For that reason, the curves are padded with data of "0.0, 0.0". The "0.0, 0.0" points should be omitted in analysis.

The expected values of $\sigma_{H}\times B(H\rightarrow\tau\tau)$ relative to the SM expectations in the total (labelled as 'Combined') and per-production-mode fit. The total $\pm1\sigma$ uncertainty in each measurement is indicated by the error bar. The total systematic uncertainty in each measurement is also indicated.

The expected values for $\sigma_{H}\times B(H\rightarrow\tau\tau)$ relative to the SM expectations in the simplified template cross-section measurement. The total $\pm1\sigma$ uncertainty in each measurement is indicated by the error bar. The total systematic uncertainty in each measurement is also indicated.

The measured values for $\sigma_{H}\times B(H\rightarrow\tau\tau)$ in the simplified template cross-section measurement. The total $\pm1\sigma$ uncertainty in the measurement is indicated by the black error bars, with the individual contribution from the statistical uncertainty in blue.

The measured correlations between differential fiducial cross-sections, $\sigma$, and the normalisation factors, $\alpha$, for the $Z(\to\tau\tau) + \text{jets}$ and top backgrounds for $p_\text{T}(j_0)$. The bins denoted as $\sigma_{1,2,3,4}$ correspond to the measurements in the four bins listed elsewhere for each observable.

The measured correlations between differential fiducial cross-sections, $\sigma$, and the normalisation factors, $\alpha$, for the $Z(\to\tau\tau) + \text{jets}$ and top backgrounds for $p_\text{T}^{H}}$. The bins denoted as $\sigma_{1,2,3,4}$ correspond to the measurements in the four bins listed elsewhere for each observable.

The measured correlations between differential fiducial cross-sections, $\sigma$, and the normalisation factors, $\alpha$, for the $Z(\to\tau\tau) + \text{jets}$ and top backgrounds for $\Delta\phi_{jj}^{\text{signed}}$. The bins denoted as $\sigma_{1,2,3,4}$ correspond to the measurements in the four bins listed elsewhere for each observable.

The measured correlations between differential fiducial cross-sections, $\sigma$, and the normalisation factors, $\alpha$, for the $Z(\to\tau\tau) + \text{jets}$ and top backgrounds for $\Delta\phi_{jj}^{\text{signed}}$ vs $p_\text{T}^{H}$. The bins denoted as $\sigma_{1,2,3,4}$ correspond to the measurements in the four bins listed elsewhere for each observable.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of the number of $b$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of the number of $l/c$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $H_{\text{T}}^{\text{had}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R_{\text{avg}}^{bb}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{3})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $p_{\text{T}}(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{min}\Delta R})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{min}\Delta R})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{3})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta R(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(e\mu bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(l/c\text{-jet}_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $\Delta\eta_{\text{max}}^{jj}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $H_{\text{T}}^{\text{all}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $m(e\mu b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{2})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{1}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least three $b$-jets as a function of $|\eta(b_{2}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{3})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{4})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{2}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $p_{\text{T}}(bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $H_{\text{T}}^{\text{all}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(e\mu b_{1}b_{2})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $m(e\mu bb^{\text{top}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $H_{\text{T}}^{\text{had}}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\text{min}\Delta R(bb)$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta R_{\text{avg}}^{bb}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $\Delta\eta_{\text{max}}^{jj}$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of the number of $l/c$-jets compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $|\eta(l/c\text{-jet}_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets and at least one $l/c$-jet as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{2})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at leastfour $b$-jets as a function of $|\eta(b_{2}^{\text{top}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{3})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{4})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{1}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

Data bootstraps post unfolding for the normalised differential cross-section in the phase space with at least four $b$-jets as a function of $|\eta(b_{2}^{\text{add}})|$ compared with predictions. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. The last bin contains the overflow.

The measured normalised differential cross-section as a function of $N_{b-\text{jets}}$ in the $e\mu+\geq2b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{had}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{all}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R_{\text{avg}}^{bb}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta\eta_{\text{max}}^{jj}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{3})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{top}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{top}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{3})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{add}})|$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu bb^{\text{top}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(b_{1}b_{2})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $N_{l/c-\text{jets}}$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq3b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}},l/c-\text{jet})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(l/c\text{-jet}_{1})|$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the $e\mu+\geq3b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{had}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $H_{\text{T}}^{\text{all}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R_{\text{avg}}^{bb}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta\eta_{\text{max}}^{jj}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{3})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{4})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{2}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{top}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{top}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{3})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{1}^{\text{add}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{4})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(b_{2}^{\text{add}})|$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(e\mu bb^{\text{top}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{min}\Delta R})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{min}\Delta R})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $m(bb^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(bb^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\text{min}\Delta R(bb)$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(b_{1}b_{2})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $N_{l/c-\text{jets}}$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the $e\mu+\geq4b$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $|\eta(l/c\text{-jet}_{1})|$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The measured normalised differential cross-section as a function of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the $e\mu+\geq4b+\geq1l/c-\text{jet}$ phase space. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{b-\text{jets}}$ in the phase space with at least two b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{b-\text{jets}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{had}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{all}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R_{\text{avg}}^{bb}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta\eta_{\text{max}}^{jj}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{3})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{3})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{add}})|$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu bb^{\text{top}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(b_{1}b_{2})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{l/c-\text{jets}}$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space with at least three b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}},l/c-\text{jet})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(l/c\text{-jet}_{1})|$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the phase space with at least three b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{had}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $H_{\text{T}}^{\text{all}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R_{\text{avg}}^{bb}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta\eta_{\text{max}}^{jj}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{3})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{4})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{2}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{top}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{top}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{3})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{1}^{\text{add}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{4})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(b_{2}^{\text{add}})|$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(e\mu bb^{\text{top}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{min}\Delta R})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{min}\Delta R})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $m(bb^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(bb^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\text{min}\Delta R(bb)$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(b_{1}b_{2})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $N_{l/c-\text{jets}}$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu b_{1}b_{2},b_{3})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, b_{1}^{\text{add}})$ in the phase space with at least four b-jets. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $\Delta R(e\mu bb^{\text{top}}, l/c\text{-jet}_{1})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1}) - p_{\text{T}}(b_{1}^{\text{add}})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $|\eta(l/c\text{-jet}_{1})|$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The correlation matrix for the measured normalised differential cross-section in terms of $p_{\text{T}}(l/c\text{-jet}_{1})$ in the phase space with at least four b-jets and at least one $l/c$-jet. The overflow is included in the last bin.

The observed profile likelihood ratio, $-2ln \Lambda$, as a function of $\Gamma_H$. Compared with the version in the paper, the effective couplings in the ggF, $H\to\gamma\gamma$, and $H\to Z\gamma$ processes are in terms of SM coupling strengths, resulting in an improved upper limit under stronger model assumptions.

The observed profile likelihood ratio, $-2ln \Lambda$, as a function of $\Gamma_H/\Gamma_H^{SM}$. Compared with the version in the paper, the effective couplings in the ggF, $H\to\gamma\gamma$, and $H\to Z\gamma$ processes are in terms of SM coupling strengths, resulting in an improved upper limit under stronger model assumptions.

The observed profile likelihood ratio, $-2ln \Lambda$, as a function of $\Gamma_H/\Gamma_H^{SM}$ and $\kappa_t$. Compared with the version in the paper, the effective couplings in the ggF, $H\to\gamma\gamma$, and $H\to Z\gamma$ processes are in terms of SM coupling strengths, resulting in an improved upper limit under stronger model assumptions.

Post-fit $m_{\mathrm{T}}$ distribution in the signal region for the SM background-only hypothesis. Data are shown as black markers with vertical error bars representing the statistical uncertainty. Filled histograms show contributions of various SM processes, with the hatched band representing the total uncertainty. The line shows the prediction of the GM model for $\sin \theta_{\mathrm{H}} = 0.17$ and $m_{\mathrm{H_5}} = 375$ GeV, where the $\sin \theta_{\mathrm{H}}$ value corresponds to the expected $95\%$ CL limit for that $H_5$ mass.

Expected and observed exclusion limits at 95% CL for $\sigma_{\mathrm{VBF}}(H_{5}^{\pm}) \times \mathcal{B}(H_{5}^{\pm} \to W^{\pm}Z)$ as a function of $m_{\mathrm{H_5}}$. The inner (outer) band represents the $68\%$ ($95\%$) confidence interval around the median expected limit.

Expected and observed exclusion limits at 95% CL for $\sigma_{\mathrm{VBF}}(H_{5}^{\pm}) \times \mathcal{B}(H_{5}^{\pm} \to W^{\pm}Z)$ as a function of $m_{\mathrm{H_5}}$. The inner (outer) band represents the $68\%$ ($95\%$) confidence interval around the median expected limit.

Expected and observed exclusion limits at 95% CL for $\sigma_{\mathrm{VBF}}(H_{5}^{\pm\pm}) \times \mathcal{B}(H_{5}^{\pm\pm} \to W^{\pm}W^{\pm})$ as a function of $m_{\mathrm{H_5}}$. The inner (outer) band represents the $68\%$ ($95\%$) confidence interval around the median expected limit.

Expected and observed exclusion limits at 95% CL for $\sigma_{\mathrm{VBF}}(H_{5}^{\pm\pm}) \times \mathcal{B}(H_{5}^{\pm\pm} \to W^{\pm}W^{\pm})$ as a function of $m_{\mathrm{H_5}}$. The inner (outer) band represents the $68\%$ ($95\%$) confidence interval around the median expected limit.

Expected and observed exclusion limits at 95% CL for $\sin \theta_{{\mathrm{H}}}$ as a function of $m_{\mathrm{H_5}}$. The inner (outer) band represents the $68\%$ ($95\%$) confidence interval around the median expected limit. The exclusion limits are shown up to $m_{\mathrm{H_5}} = 1500$ GeV. The limits obtained separately in the $H_{5}^{\pm} \to W^{\pm}Z$ and $H_{5}^{\pm\pm} \to W^{\pm}W^{\pm}$ channels are also shown for comparison. The hatched region covers the parameter space where the intrinsic widths of the $H_{5}^{\pm}$ and $H_{5}^{\pm\pm}$ bosons would be larger than $10\%$ of $m_{\mathrm{H_5}}$ and is disfavoured in the GM model.

Expected and observed exclusion limits at 95% CL for $\sin \theta_{{\mathrm{H}}}$ as a function of $m_{\mathrm{H_5}}$. The inner (outer) band represents the $68\%$ ($95\%$) confidence interval around the median expected limit. The exclusion limits are shown up to $m_{\mathrm{H_5}} = 1500$ GeV. The limits obtained separately in the $H_{5}^{\pm} \to W^{\pm}Z$ and $H_{5}^{\pm\pm} \to W^{\pm}W^{\pm}$ channels are also shown for comparison. The hatched region covers the parameter space where the intrinsic widths of the $H_{5}^{\pm}$ and $H_{5}^{\pm\pm}$ bosons would be larger than $10\%$ of $m_{\mathrm{H_5}}$ and is disfavoured in the GM model.

Signal selection efficiency times acceptance for charged Higgs boson with masses between $60\,$GeV to $168\,$GeV. The efficiency times acceptance drops at high masses rapidly due to the low momentum of the $b$-quark produced together with the charged Higgs boson. For the $168\,$GeV mass point the efficiency times acceptance is larger than for the $160\,$GeV mass point due to having a higher probability that the top quark, which decays into the charged Higgs boson and a $b$-quark, is off-shell.

Best fit values of $\mu_{t\bar{t}}$ and $f_{\mathrm{HF}}$ parameters at charged Higgs masses between $60\,$GeV and $168\,$GeV.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a selection of models targeted in the CalR+W channel for (a) WALP models and (b) WHS models with a SM Higgs boson mediator.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a selection of models targeted in the CalR+W channel for (a) WALP models and (b) WHS models with a SM Higgs boson mediator.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a variety of models probed by the CalR+Z channel for (a) ZALP models, (b) ZHS models with the SM Higgs boson as a mediator, and (c) HZZ_d models.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a variety of models probed by the CalR+Z channel for (a) ZALP models, (b) ZHS models with the SM Higgs boson as a mediator, and (c) HZZ_d models.

Observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on the cross-section times branching fraction as a function of c&tau; for a variety of models probed by the CalR+Z channel for (a) ZALP models, (b) ZHS models with the SM Higgs boson as a mediator, and (c) HZZ_d models.

Summary of the observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on C_G as a function of C_W for a variety of ALP mass hypotheses probed by the CalR+R and Cal+Z channels for (a) WALP and (b) ZALP models.

Summary of the observed (solid line) and expected (dashed line) upper limits at the 95&percnt; CL on C_G as a function of C_W for a variety of ALP mass hypotheses probed by the CalR+R and Cal+Z channels for (a) WALP and (b) ZALP models.

Regions in the ALP mass versus its coupling to gluons scaled by the ALP energy scale g_agg=4C_G/f_a excluded at 95&percnt; CL, for C_W=1, C_W=0.5, C_W=0.3 and C_W=0.15, in the (a) WALP and (b) ZALP models. The contours are obtained by interpolating between the exclusions for different LLP masses considered in the analysis, taking the logical union of the regions excluded for each mass.

Regions in the ALP mass versus its coupling to gluons scaled by the ALP energy scale g_agg=4C_G/f_a excluded at 95&percnt; CL, for C_W=1, C_W=0.5, C_W=0.3 and C_W=0.15, in the (a) WALP and (b) ZALP models. The contours are obtained by interpolating between the exclusions for different LLP masses considered in the analysis, taking the logical union of the regions excluded for each mass.

Signal efficiencies as a function of c&tau; for HS signals in the CalR+2J channel for samples with mediator masses (a) below 400 GeV and (b) equal to or above 400 GeV. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for HS signals in the CalR+2J channel for samples with mediator masses (a) below 400 GeV and (b) equal to or above 400 GeV. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for WALP and WHS signals in the CalR+W channel for (a) the WALP selection, (b) the CalR+W low-E_T selection and (c) the CalR+W high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for WALP and WHS signals in the CalR+W channel for (a) the WALP selection, (b) the CalR+W low-E_T selection and (c) the CalR+W high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for WALP and WHS signals in the CalR+W channel for (a) the WALP selection, (b) the CalR+W low-E_T selection and (c) the CalR+W high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for ZALP, HZZ_d and ZHS signals in the CalR+Z channel for (a, b) the CalR+Z low-E_T selection and (c, d) the CalR+Z high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for ZALP, HZZ_d and ZHS signals in the CalR+Z channel for (a, b) the CalR+Z low-E_T selection and (c, d) the CalR+Z high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for ZALP, HZZ_d and ZHS signals in the CalR+Z channel for (a, b) the CalR+Z low-E_T selection and (c, d) the CalR+Z high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

Signal efficiencies as a function of c&tau; for ZALP, HZZ_d and ZHS signals in the CalR+Z channel for (a, b) the CalR+Z low-E_T selection and (c, d) the CalR+Z high-E_T selection. The extrapolation of efficiencies to new mean LLP lifetimes is done via the event reweighting procedure. Only statistical uncertainties are shown.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed black line) and observed (solid line) upper limits on the cross-section times branching fraction in the CalR+2J channel for the benchmark HS models with mass pairs (m_&Phi;, m_S) (a) (60, 16), (b) (125, 35), (c) (125, 55), (d) (200, 50), (e) (400, 100), (f) (600, 150), (g) (600, 275), (h) (1000, 275), and (i) (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) upper limits on the cross-section times branching fraction in the WHS channel for the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (60, 5) and (60, 15), (b) (200, 50) and (200, 80), (c) (400, 100) and (400, 175), (d) (600, 50), (600, 150), and (600, 275), (e) (1000, 50), (1000, 275), and (1000, 475) GeV

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (125, 16), (b) (600, 150) GeV, and the WALP selection for WALP models with (c) m_ALP = 1 GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (125, 16), (b) (600, 150) GeV, and the WALP selection for WALP models with (c) m_ALP = 1 GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the low-E_T WHS selection for benchmark WHS models with mass pairs (m_&Phi;, m_S) (a) (125, 16), (b) (600, 150) GeV, and the WALP selection for WALP models with (c) m_ALP = 1 GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

95&percnt; CL expected (dashed line) and observed (solid line) limits on the branching fraction in the CalR+Z channel for benchmark HZZ_d models with mass pairs (m_&Phi;, m_{Z_d}) (a) (400, 100) and (400, 200), (b) (600, 150) and (600, 400). The ZHS sample is shown for masses (m_&Phi;, m_S) in GeV: (c) (200, 50) and (200, 80), (d) (400, 100) and (400, 175), (e) (600, 50), (600, 150) and (600, 275), (f) (1000, 50), (1000, 275) and (1000, 475) GeV.

Data from Figure 1, panel c, $k_{2}$ vs $N_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 1, panel c, $k_{2}$ vs $N_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 1, panel d, $k_{3}$ vs $N_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 1, panel d, $k_{3}$ vs $N_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 2, panel a, $N_{ch}k_{2}$ vs $N_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 2, panel a, $N_{ch}k_{2}$ vs $N_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 2, panel b, $N^{2}_{ch}k_{3}$ vs $N_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 2, panel b, $N^{2}_{ch}k_{3}$ vs $N_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 2, panel c, $\Gamma$ vs $N_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 2, panel c, $\Gamma$ vs $N_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 3, panel a, $\left\langle[p_{T}]\right\rangle / \left\langle[p_{T}]\right\rangle^{5\%}$ vs $N_{ch}/N^{5\%}_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 3, panel a, $\left\langle[p_{T}]\right\rangle / \left\langle[p_{T}]\right\rangle^{5\%}$ vs $N_{ch}/N^{5\%}_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 3, panel b, $k_{2}/k^{5\%}_{2} vs N_{ch}/N^{5\%}_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 3, panel b, $k_{2}/k^{5\%}_{2} vs N_{ch}/N^{5\%}_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 3, panel c, $k_{3}/k^{5\%}_{3} vs N_{ch}/N^{5\%}_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 3, panel c, $k_{3}/k^{5\%}_{3} vs N_{ch}/N^{5\%}_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 3, panel d, $\Gamma/\Gamma^{5\%}$ vs $N_{ch}/N^{5\%}_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 3, panel d, $\Gamma/\Gamma^{5\%}$ vs $N_{ch}/N^{5\%}_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 4, $\Delta p_{T}/\left\langle[p_{T}]\right\rangle_{0-1\%}$ vs $\Delta N_{ch}/\left\langle N_{ch}\right\rangle_{0-1\%}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 4, $\Delta p_{T}/\left\langle[p_{T}]\right\rangle_{0-1\%}$ vs $\Delta N_{ch}/\left\langle N_{ch}\right\rangle_{0-1\%}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Figure 4, $\Delta p_{T}/\left\langle[p_{T}]\right\rangle_{0-1\%}$ vs $\Delta N_{ch}/\left\langle N_{ch}\right\rangle_{0-1\%}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 2 GeV/c, $|\eta|< $ 2.5

Data from Figure 4, $\Delta p_{T}/\left\langle[p_{T}]\right\rangle_{0-1\%}$ vs $\Delta N_{ch}/\left\langle N_{ch}\right\rangle_{0-1\%}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 2 GeV/c, $|\eta|< $ 2.5

Data from Second Supplementary Figure, Fig 6, panel a, Correlation between FCal $\Sigma E_{T}$ and $N_{ch}$ in Pb+Pb collisions

Data from Second Supplementary Figure, Fig 6, panel b, Correlation between $[p_{T}]$ - $\left\langle[p_{T}]\right\rangle_{0-1\%}$ vs $\Delta N_{ch}$/$\left\langle N_{ch}\right\rangle_{0-1\%}$ in Pb+Pb collisions

Data from Third Supplementary Figure, Fig 7, Normalized Event distribution of $N_{ch}$ /$N^{5\%}_{ch}$ for Pb+Pb collisions

Data from Third Supplementary Figure, Fig 7, Normalized Event distribution of $N_{ch}$ / $N^{5\%}_{ch}$ for Xe+Xe collisions

Data from Fourth Supplementary Figure, Fig 8, panel a, $\gamma$ vs $N_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Fourth Supplementary Figure, Fig 8, panel a, $\gamma$ vs $N_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Fourth Supplementary Figure, Fig 8, panel b, $\gamma/\gamma^{5\%}$ vs $N_{ch}/N^{5\%}_{ch}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Fourth Supplementary Figure, Fig 8, panel b, $\gamma/\gamma^{5\%}$ vs $N_{ch}/N^{5\%}_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Auxiliary Fig 1 panel a, Correlation between FCal $\Sigma E_{T}$ and $N_{ch}$ in Xe+Xe collisions

Data from Auxiliary Fig 1 panel b, Correlation between $[p_{T}]$ - $\left\langle[p_{T}]\right\rangle_{0-1\%}$ vs $\Delta N_{ch}$/$\left\langle N_{ch}\right\rangle_{0-1\%}$ in Xe+Xe collisions

Data from Auxiliary Fig 2 panel b, $N_{ch}$ /$N^{5\%}_{ch}$ vs Centrality [\%] for Pb+Pb collisions

Data from Auxiliary Fig 2 panel b, $N_{ch}$ /$N^{5\%}_{ch}$ vs Centrality [\%] for Xe+Xe collisions

Data from Auxiliary Fig 3 panel b, $[p_{T}]$ vs $N_{ch}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Auxiliary Figure 4, $\Delta p_{T}/\left\langle[p_{T}]\right\rangle_{0-0.5\%}$ vs $\Delta N_{ch}/\left\langle N_{ch}\right\rangle_{0-0.5\%}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Auxiliary Figure 4, $\Delta p_{T}/\left\langle[p_{T}]\right\rangle_{0-0.5\%}$ vs $\Delta N_{ch}/\left\langle N_{ch}\right\rangle_{0-0.5\%}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 5 GeV/c, $|\eta|< $ 2.5

Data from Auxiliary Figure 4, $\Delta p_{T}/\left\langle[p_{T}]\right\rangle_{0-0.5\%}$ vs $\Delta N_{ch}/\left\langle N_{ch}\right\rangle_{0-0.5\%}$ for Pb+Pb collisions, 0.5 $ <p_{T}< $ 2 GeV/c, $|\eta|< $ 2.5

Data from Auxiliary Figure 4, $\Delta p_{T}/\left\langle[p_{T}]\right\rangle_{0-0.5\%}$ vs $\Delta N_{ch}/\left\langle N_{ch}\right\rangle_{0-0.5\%}$ for Xe+Xe collisions, 0.5 $ <p_{T}< $ 2 GeV/c, $|\eta|< $ 2.5

The pNN input variable visible transverse momentum $p_{\mathrm{T}}^{\mathrm{vis}}(\mu\tau_{\mathrm{had}})$ is shown in the SR with no cut on the pNN discriminant. The signal shape is normalized to the same integral as the total background prediction. Overflow events are included in the last bins.

The pNN input variable transverse mass $m_{\mathrm{T}}(\mu,E_{\mathrm{T}}^{\mathrm{miss}})$ is shown in the SR with no cut on the pNN discriminant. The signal shape is normalized to the same integral as the total background prediction. Overflow events are included in the last bins.

The pNN input variable MMC mass $m^{\mathrm{MMC}}(bb\tau\tau)$ is shown in the SR with no cut on the pNN discriminant. The signal shape is normalized to the same integral as the total background prediction. Overflow events are included in the last bins.

Mass distribution of the $\tau^+\tau^-$ system for each of the generated MC signal mass points for the $\mu\tau_{\mathrm{h}}$ category. All distributions are normalized assuming $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=10\%$.

Mass distribution of the $\tau^+\tau^-$ system for each of the generated MC signal mass points for the $e\mu$ category. All distributions are normalized assuming $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=10\%$.

The pNN spectrum in the three bins are shown separately for each mass hypothesis in the ($e\mu$, $1b$) category. The signal shape (normalized to $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=1$) for each corresponding mass hypothesis is overlaid on top of the SM prediction. Bins used for different $m_a$ hypotheses are not statistically independent.

The pNN spectrum in the three bins are shown separately for each mass hypothesis in the ($\mu\tau_{\mathrm{had}}$,$2b$) category. The signal shape (normalized to $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=1$) for each corresponding mass hypothesis is overlaid on top of the SM prediction. Bins used for different $m_a$ hypotheses are not statistically independent.

The pNN spectrum in the three bins are shown separately for each mass hypothesis in the ($e\mu$,$1B$) category. The signal shape (normalized to $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=1$) for each corresponding mass hypothesis is overlaid on top of the SM prediction. Bins used for different $m_a$ hypotheses are not statistically independent.

The pNN spectrum in the three bins are shown separately for each mass hypothesis in the ($e\tau_{\mathrm{had}}$,$2b$) category. The signal shape (normalized to $\mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)=1$) for each corresponding mass hypothesis is overlaid on top of the SM prediction. Bins used for different $m_a$ hypotheses are not statistically independent.

The observed (solid) 95% C.L. upper limits on $(\sigma(H)/\sigma_{\mathrm{SM}}(H)) \mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)$ as a function of $m_a$ and the expected (dashed) limits under the background-only hypothesis when combining all categories. The inner green and outer yellow shaded bands show the $\pm 1\sigma$ and $\pm 2\sigma$ uncertainties of the expected limits.

The observed (solid) 95% C.L. upper limits on $(\sigma(H)/\sigma_{\mathrm{SM}}(H)) \mathcal{B}(H\rightarrow aa\rightarrow b\bar{b}\tau^+\tau^-)$ as a function of $m_a$ and the expected (dashed) limits under the background-only hypothesis when considering different categories based on the heavy-flavor objects separately. When the category is not used for a mass hypothesis, the limit is listed as zero.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\gamma\gamma$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\ell\ell+E_\text{T}^{\text{miss}}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for Multilepton.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for combination.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}b\bar{b}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\tau\tau$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\gamma\gamma$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\ell\ell+E_\text{T}^{\text{miss}}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for Multilepton.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for combination.

Observed and expected 95&percnt; CL upper limits on the signal strength for the inclusive ggF HH production from the bb&#772;&tau;⁺&tau;^-, bb&#772;&gamma;&gamma;, bb&#772;bb&#772;, multilepton and bb&#772;&#8467;&#8467;+E_T^miss decay channels, and their statistical combination. When deriving the limit on &mu;_ggF^HH (&mu;_VBF^HH), the VBF (ggF) HH production cross-section is fixed to the SM predicted value for m_H=125 GeV. The expected limit, along with the &plusmn;1&sigma; and &plusmn;2&sigma; bands, is calculated under the assumption of no HH process and with all NPs profiled to the observed data.

Observed and expected 95&percnt; CL upper limits on the signal strength for the VBF HH production from the bb&#772;&tau;⁺&tau;^-, bb&#772;&gamma;&gamma;, bb&#772;bb&#772;, multilepton and bb&#772;&#8467;&#8467;+E_T^miss decay channels, and their statistical combination. When deriving the limit on &mu;_ggF^HH (&mu;_VBF^HH), the VBF (ggF) HH production cross-section is fixed to the SM predicted value for m_H=125 GeV. The expected limit, along with the &plusmn;1&sigma; and &plusmn;2&sigma; bands, is calculated under the assumption of no HH process and with all NPs profiled to the observed data.

Observed and expected upper limits at 95&percnt; CL on the inclusive ggF and VBF HH production cross-section from the bb&#772;&tau;⁺&tau;^-, bb&#772;&gamma;&gamma;, bb&#772;bb&#772;, multilepton and bb&#772;&#8467;&#8467;+E_T^miss decay channels, and their statistical combination. The Higgs boson mass is set to 125&nbsp;GeV when deriving the predicted SM cross-section (red band). The expected limits, along with the &plusmn;1&sigma; and &plusmn;2&sigma; bands, are calculated under the assumption of no Higgs boson pair production and with all NPs profiled to the observed data.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}b\bar{b}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\tau\tau$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\gamma\gamma$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for $b\bar{b}\ell\ell+E_\text{T}^{\text{miss}}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for Multilepton.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_\lambda$ parameter for combination.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}b\bar{b}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\tau\tau$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\gamma\gamma$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for $b\bar{b}\ell\ell+E_\text{T}^{\text{miss}}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for Multilepton.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $\kappa_{2V}$ parameter for combination.

Observed (solid lines) and expected (dashed lines) 95&percnt; CL exclusion limits on the HH production cross-sections of (a) the inclusive ggF and VBF processes as a function of &kappa;_&lambda;, for the bb&#772;&gamma;&gamma; (purple), bb&#772;&tau;⁺&tau;^- (green), multilepton (cyan), bb&#772;bb&#772; (blue) and bb&#772;&#8467;&#8467;+E_T^miss (brown) decay channels and their combination (black). The expected limits assume no HH production. The red line shows the theory prediction for the ggF and VBF HH production cross-section as a function of &kappa;_&lambda;. The bands surrounding the red cross-section lines indicate the theoretical uncertainties on the predicted cross-section.

Observed (solid lines) and expected (dashed lines) 95&percnt; CL exclusion limits on the HH production cross-sections of the VBF process as a function of &kappa;_2V, for the bb&#772;&gamma;&gamma; (purple), bb&#772;&tau;⁺&tau;^- (green), multilepton (cyan), bb&#772;bb&#772; (blue) and bb&#772;&#8467;&#8467;+E_T^miss (brown) decay channels and their combination (black). The expected limits assume no VBF HH production. The ggF HH production cross-section is assumed to be as predicted by the SM. The ggF HH production cross-section is assumed to be as predicted by the SM. The red line shows the predicted VBF HH cross-section as a function of &kappa;_2V. The bands surrounding the red cross-section lines indicate the theoretical uncertainties on the predicted cross-section.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}b\bar{b}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}\tau\tau$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}\gamma\gamma$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for combination.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}b\bar{b}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}\tau\tau$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for $b\bar{b}\gamma\gamma$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{gghh}$ parameter for combination.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}b\bar{b}$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}\tau\tau$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}\gamma\gamma$.

Observed value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for combination.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}b\bar{b}$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}\tau\tau$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for $b\bar{b}\gamma\gamma$.

Expected value of the test statistic (-2ln$\Lambda$), as a function of the $c_{tthh}$ parameter for combination.

Observed and expected 95&percnt; CL combined upper limits on the cross-section for the SM and seven BSM HEFT benchmarks (arXiv:2304.01968 [hep-ph]) in the ggF process, describing representative signal kinematics and m_HH shape features obtained by varying multiple HEFT coefficients. The expected limits from the bb&#772;&tau;⁺&tau;^-, bb&#772;&gamma;&gamma; and bb&#772;bb&#772; decay channels are presented as well. Theoretical predictions, estimated using specific sets of coefficient values defined in the benchmarks, are shown as red cross dots.

R32 for $H_{T2}$, 0.2 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, pT,jet

R32 for delta y

R32 for delta y max

R32 for $m_{jj}$

R32 for $m_{jj, max}$

R43 for $H_{T2}$, 60 GeV < $p_{T,3}$

R43 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R43 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R43 for $H_{T2}$, pT,jet

R43 for $Delta y_{jj}$

R43 for $Delta y_{jj, max}$

R43 for $m_{jj}$

R43 for $m_{jj, max}$

R54 for $H_{T2}$, 60 GeV < $p_{T,3}$

R54 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R54 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R54 for $Delta y_{jj}$

R54 for $Delta y_{jj, max}$

R54 for $m_{jj}$

R54 for $m_{jj, max}$

R42 for $H_{T2}$, 60 GeV < $p_{T,3}$

R42 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R42 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

R42, pT,jet

R42 for $Delta y_{jj}$

R42 for $Delta y_{jj, max}$

R42 for $m_{jj}$

R42 for $m_{jj}$

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >= 2

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >=3

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >=4

HT2, $p_{T,3}$ > 60 GeV, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.05, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.10, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.20, $N_{jet}$ >= 5

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >= 2

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >=3

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >=4

HT2, $p_{T,3}$/HT2 > 0.30, $N_{jet}$ >= 5

pTnincl

pTnincl

pTnincl

mjj max, $N_{jet}$ >= 2

mjj max, $N_{jet}$ >= 3

mjj max, $N_{jet}$ >= 4

mjj max, $N_{jet}$ >= 5

$m_{jj}$ $N_{jet}$ >= 2

$m_{jj}$ $N_{jet}$ >= 3

$m_{jj}$ $N_{jet}$ >= 4

$m_{jj}$ $N_{jet}$ >= 5

$Delta y_{jj}$ $N_{jet}$ >= 2

$Delta y_{jj}$ $N_{jet}$ >= 3

$Delta y_{jj}$ $N_{jet}$ >= 4

$Delta y_{jj}$ $N_{jet}$ >= 5

$Delta y_{jj, max}$, $N_{jet}$ >= 2

$Delta y_{jj, max}$, $N_{jet}$ >= 3

$Delta y_{jj, max}$, $N_{jet}$ >= 4

$Delta y_{jj, max}$, $N_{jet}$ >= 5

R32 for $H_{T2}$, 60 GeV < $p_{T,3}$

R32 for $H_{T2}$, 0.05 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.1 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.2 x $H_{T2} < $p_{T,3}$

R32 for $H_{T2}$, 0.3 x $H_{T2} < $p_{T,3}$

Differential cross-section in bins of subleading muon $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading muon $\eta$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading muon $\eta$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading muon $\phi$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading muon $\phi$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet $p_\mathrm{T]$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet rapidity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet rapidity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet azimuth. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet azimuth. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet mass. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet mass. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet constituent multiplicity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet constituent multiplicity. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $\tau_1$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet $\tau_1$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $\tau_2$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet $\tau_2$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $\tau_3$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of subleading charged particle jet $\tau_3$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of leading charged particle jet $\tau_{21}$. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Differential cross-section in bins of $\Delta R$ between the leading charged particle jet and the dilepton system. The actual measurement is unbinned and available with examples at <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024">gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024</a>

Distribution of the BDT output score in the the 3l channel signal region.

Distribution of the BDT output score in the 2lSC channel signal region.

Distribution of the BDT output score in the 2lSC+$\tau_{had}$ channel signal region.

Distribution of the BDT output score in the the 2l+2$\tau_{had}$ channel signal region.

Distribution of the BDT output score in the l+2$\tau_{had}$ channel signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+2(l,$\tau_{had}$) channel signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$e/\mu$ channel in the loose BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$e/\mu$ channel in the medium BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$e/\mu$ channel in the tight BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$\tau$ channel in the loose BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$\tau$ channel in the medium BDT signal region.

Invariant mass of the diphoton system in the $\gamma\gamma$+$\tau$ channel in the tight BDT signal region.

Observed and expected 95% CL upper limits on the signal strength for the ML channels and the $\gamma\gamma$+ML channels.

Expected values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the combination. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the combination. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the combination. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the combination. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the $\gamma\gamma$+ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_\lambda$ for the $\gamma\gamma$+ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Expected values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the $\gamma\gamma$+ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Observed values of -2ln$\Lambda$ as a function of $\kappa_{2V}$ for the $\gamma\gamma$+ML channels. All other coupling modifiers are fixed to their SM predictions and the expected limits are computed assuming the SM.

Statistical covariance between bins of Table 2

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 3

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 4

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 5

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 6

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 7

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 8

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 9

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 10

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 11

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 12

Mean-$p_{T}$ of $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 13

Mean-$p_{T}$ of $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 14

Mean-$p_{T}$ of $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 15

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 16

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 17

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 18

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 19

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 20

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 21

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 22

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 23

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 24

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 25

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 26

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 27

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 28

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 29

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 30

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 31

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 32

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 33

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 34

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 35

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 36

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 37

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 38

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 39

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 40

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 41

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 42

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 43

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 44

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 45

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 46

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 47

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 48

Mean-$p_{T}$ of $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 49

Mean-$p_{T}$ of $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 50

Mean-$p_{T}$ of $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 51

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 52

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 53

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 54

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 55

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 56

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 57

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 58

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 59

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 60

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 61

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 62

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 63

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 64

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 65

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 66

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 67

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 68

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 69

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 70

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 71

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 72

The figure shows the post-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb$^{-1}$. The data correspond to the $1\ell 1b$ $\mu$+jets channel in a pre-fit configuration. The stacked histograms represent different processes contributing to the event yield, including top quark pair production ($t\bar{t}$), single top, $W$ boson production with $b$, $c$, and light quarks, $Z$ boson production with $b$, $c$, and light quarks, diboson, and fake lepton backgrounds.

The figure shows the pre-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb$^{-1}$. The data correspond to the $1\ell 2bincl$ $e$+jets channel in a pre-fit configuration. The stacked histograms represent different processes contributing to the event yield, including top quark pair production ($t\bar{t}$), single top, $W$ boson production with $b$, $c$, and light quarks, $Z$ boson production with $b$, $c$, and light quarks, diboson, and fake lepton backgrounds.

The figure shows the post-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb$^{-1}$. The data correspond to the $1\ell 2bincl$ $e$+jets channel in a pre-fit configuration. The stacked histograms represent different processes contributing to the event yield, including top quark pair production ($t\bar{t}$), single top, $W$ boson production with $b$, $c$, and light quarks, $Z$ boson production with $b$, $c$, and light quarks, diboson, and fake lepton backgrounds.

The figure shows the pre-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb$^{-1}$. The data correspond to the $1\ell 2bincl$ $\mu$+jets channel in a pre-fit configuration. The stacked histograms represent different processes contributing to the event yield, including top quark pair production ($t\bar{t}$), single top, $W$ boson production with $b$, $c$, and light quarks, $Z$ boson production with $b$, $c$, and light quarks, diboson, and fake lepton backgrounds.

The figure shows the post-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb$^{-1}$. The data correspond to the $1\ell 2bincl$ $\mu$+jets channel in a pre-fit configuration. The stacked histograms represent different processes contributing to the event yield, including top quark pair production ($t\bar{t}$), single top, $W$ boson production with $b$, $c$, and light quarks, $Z$ boson production with $b$, $c$, and light quarks, diboson, and fake lepton backgrounds.

The figure shows the pre-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb\(^{-1}\) in the 2\(\ell\)1b signal region. The histogram represents the number of events per GeV, with the data points shown as black dots and their associated uncertainties. The stacked colored histograms indicate different processes: \(t\bar{t}\) (red), single top (orange), \(Z+b\) (dark blue), \(Z+c\) (medium blue), \(Z+\)light (light blue), diboson (gray), and fake lepton (purple). The hatched area represents the uncertainty in the predictions.

The figure shows the post-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb\(^{-1}\) in the 2\(\ell\)1b signal region. The histogram represents the number of events per GeV, with the data points shown as black dots and their associated uncertainties. The stacked colored histograms indicate different processes: \(t\bar{t}\) (red), single top (orange), \(Z+b\) (dark blue), \(Z+c\) (medium blue), \(Z+\)light (light blue), diboson (gray), and fake lepton (purple). The hatched area represents the uncertainty in the predictions.

The figure shows the pre-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb\(^{-1}\) in the 2\(\ell\)2bincl signal region. The histogram represents the number of events per GeV, with the data points shown as black dots and their associated uncertainties. The stacked colored histograms indicate different processes: \(t\bar{t}\) (red), single top (orange), \(Z+b\) (dark blue), \(Z+c\) (medium blue), \(Z+\)light (light blue), diboson (gray), and fake lepton (purple). The hatched area represents the uncertainty in the predictions.

The figure shows the post-fit distribution of events as a function of $H_{\mathrm{T}}^{\ell,j} = \sum_{\ell,j} p_{T}^{\ell,j}$, scalar sum of $p_T$ for all jets and leptons in the $\ell+$jets channel, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, with an integrated luminosity of 165 nb\(^{-1}\) in the 2\(\ell\)2bincl signal region. The histogram represents the number of events per GeV, with the data points shown as black dots and their associated uncertainties. The stacked colored histograms indicate different processes: \(t\bar{t}\) (red), single top (orange), \(Z+b\) (dark blue), \(Z+c\) (medium blue), \(Z+\)light (light blue), diboson (gray), and fake lepton (purple). The hatched area represents the uncertainty in the predictions.

The correlation matrix of the fit parameters for the six combined regions, including the signal regions (electron+jets: 1-lepton 1-bjet and 1-lepton 2-bjet inclusive, muon+jets: 1-lepton 1-bjet and 1-lepton 2-bjet inclusive, dilepton: 2-lepton 1-bjet and 2-lepton 2-bjet inclusive) is shown.

The impact of systematic uncertainties on the fitted signal-strength parameter $\hat{\mu}$ for the combined (six signal regions ($e+$jets: $1\ell1b$ and $1\ell2b\mathrm{incl}$, $\mu+$jets: $1\ell1b$ and $1\ell2b\mathrm{incl}$, dilepton: $2\ell1b$ and $2\ell2b\mathrm{incl}$) fit of all channels. Only the 15 most significant systematic uncertainties are shown and listed in decreasing order of their impact on $\mu$ on the $y$-axis. The empty (filled) blue/cyan boxes correspond to the pre-fit (post-fit) impact on $\mu$, referring to the upper $x$-axis. The impact of each systematic uncertainty, $\Delta \mu$, is calculated by comparing the nominal best-fit value of $\mu$ with the result of the fit when fixing the corresponding nuisance parameter $\theta$ to its best-fit value $\hat{\theta}$ shifted by its pre-fit (post-fit) uncertainties $\hat{\theta} \pm \Delta \theta(\hat{\theta} \pm \Delta \hat{\theta})$. The black points, which refer to the lower $x$-axis, show the pulls of the fitted nuisance parameters, i.e., the deviations of the fitted parameters $\hat{\theta}$ from their nominal values $\theta_0$, normalized to their nominal uncertainties $\Delta \theta$. The black lines show the post-fit uncertainties of the nuisance parameters, relative to their nominal uncertainties, which are indicated by the dashed lines.

Breakdown of relative systematic uncertainties in the measured ${t\bar{t}}$ cross-section. The quoted uncertainties are obtained by repeating the fit with a group of nuisance parameters fixed to their fitted values and subtracting in quadrature the resulting total uncertainty from the uncertainty of the complete fit. However, the total uncertainty is not the quadratic sum of the grouped impacts, as this approach neglects the correlation among the different groups.

The signal strength $\mu_{t\bar{t}}$, defined as the ratio of the observed signal for the combined six signal regions ($e+$jets: $1\ell1b$ and $1\ell2b\mathrm{incl}$, $\mu+$jets: $1\ell1b$ and $1\ell2b\mathrm{incl}$, dilepton: $2\ell1b$ and $2\ell2b\mathrm{incl}$.) to the SM expectation with no nPDF effects included, is measured using a binned profile-likelihood method. The parameter $\mu_{t\bar{t}}$ is determined by the fit to the $H_{T}^{\ell,j}$ data distributions in the six signal regions, where the $H_{T}^{\ell,j}$ variable is defined as the scalar sum of the transverse momenta of the leptons and HI jets. Over all 9% uncertainties is observed

The figure shows the measurement of the top quark pair production cross-section, $\sigma_{t\bar{t}}$, in proton-lead (p+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 8.16$ TeV, based on 165 nb$^{-1}$ of data. The central measured value is shown alongside theoretical predictions using various parton distribution functions (PDFs): MCFM TUJU21, MCFM nNNPDF30, MCFM nCTEQ15HQ, and MCFM EPPS21. The green and yellow bands represent the total and statistical uncertainties of the data, respectively. Additional measurements for comparison include the CMS result at 8.16 TeV for p+Pb collisions and an extrapolated ATLAS+CMS result for pp collisions at 8 TeV. Relevant references are indicated: PRL 119 (2017) 242001 and JHEP 07 (2023) 213.

The ATLAS measurement of \( R_{p\text{Pb}} \) as a function of the nuclear modification factor in proton-lead (\( p\) + Pb) collisions at a center-of-mass energy per nucleon pair \( \sqrt{s_{NN}} = 8.16 \) TeV, with an integrated luminosity of 165 nb\(^{-1}\). The black points represent theoretical predictions from various MCFM parton distribution functions (PDFs): TUJU21, nNNPDF30, nCTEQ15HQ, and EPPS21. The green band indicates the total uncertainty of the data, while the yellow band represents the statistical uncertainty. The dashed line at \( R_{p\text{Pb}} = 1 \) shows the expected value in the absence of nuclear modifications.

The mass planes of the reconstructed Higgs boson candidates for the 2Pass selections of the analysis, shown for the VBF $\kappa_{2V} = 0$ HH samples.

The mass planes of the reconstructed Higgs boson candidates for the 2Pass selections of the analysis, shown for the $m_{X} = 1$ TeV spin-0 narrow-width resonance HH samples.

Observed values of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the resolved (dotted green) and boosted (dashed blue) analyses, and their combination (solid black), with all other coupling modifiers fixed to their SM predictions.

Expected values of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the resolved (dotted green) and boosted (dashed blue) analyses, and their combination (solid black), with all other coupling modifiers fixed to their SM predictions.

Observed $-2\ln\Lambda$ values of boosted-only result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Observed $-2\ln\Lambda$ values of resolved-only result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Observed $-2\ln\Lambda$ values of combination result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Expected $-2\ln\Lambda$ values of boosted-only result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Expected $-2\ln\Lambda$ values of resolved-only result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Expected $-2\ln\Lambda$ values of combination result in the $\kappa_{\Lambda}-\kappa_{2V}$ plane.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of spin-0 heavy resonances with narrow width assumptions. The SM H → bb branching ratio is assumed in both cases. The ±1$\sigma$ and ±2$\sigma$ uncertainty ranges for the expected limits are shown as coloured bands.

Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of spin-0 heavy resonances with broad width assumptions. The SM H → bb branching ratio is assumed in both cases. The ±1$\sigma$ and ±2$\sigma$ uncertainty ranges for the expected limits are shown as coloured bands.

Observed and expected 95% CL exclusion limits on the production cross-sections of the combined VBF HH process as a function of $\kappa_{2V}$ . The expected exclusion limits assume no HH production and are obtained using a fit to the data with the background-only hypothesis. The red line shows the theory prediction for the VBF HH cross-section as a function of $\kappa_{2V}$ where all other parameters and couplings are set to their SM values.

Observed and expected 95% CL exclusion limits on the production cross-sections of the combined HH process as a function of $\kappa_{\lambda}$ . The expected exclusion limits assume no HH production and are obtained using a fit to the data with the background-only hypothesis. The red line shows the theory prediction for the HH cross-section as a function of $\kappa_{\lambda}$ where all other parameters and couplings are set to their SM values.

Observed −2 ln Λ as a function of $\kappa_{\lambda}$ with the $\kappa_{2V}$ and $\kappa_{V}$ modifiers fixed to unity.

Expected −2 ln Λ as a function of $\kappa_{\lambda}$ with the $\kappa_{2V}$ and $\kappa_{V}$ modifiers fixed to unity.

Cumulative acceptance times efficiency as a function of resonance mass for each event selection step for the narrow-width signal

Cumulative acceptance times efficiency as a function of resonance mass for each event selection step for the broad-width signal

Cumulative acceptance times efficiency as a function of $\kappa_{2V}$ for each event selection step for the nonresonant signal.

The distributions of the invariant mass of the HH system for various values of $\kappa_{2V}$ .

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the combined fit, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the combined fit, when all other coupling modifiers are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the combined fit, when all other coupling modifiers are fixed to their SM predictions.

Likelihood observed contours at 68% CL (solid line) and 95% CL (dashed line) in the ($\kappa_{\lambda}$, $\kappa_{2V}$) parameter space, when all other coupling modifiers are fixed to their SM predictions. The best-fit value, denoted by a cross in the plot, corresponds to the very first entry in the table.

Likelihood expected contours at 68% CL (teal shaded region) and 95% CL (yellow shaded region) in the ($\kappa_{\lambda}$, $\kappa_{2V}$) parameter space, when all other coupling modifiers are fixed to their SM predictions. In the plot, the SM prediction is indicated by the star.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{hhh}$, $c_{gghh}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross. The best-fit value corresponds to the very first entry in the table.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{hhh}$, $c_{gghh}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{hhh}$, $c_{tthh}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross. The best-fit value corresponds to the very first entry in the table.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{hhh}$, $c_{tthh}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{H}$, $c_{H◻}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross. The best-fit value corresponds to the very first entry in the table.

Likelihood contours at 68% (solid line) and 95% (dashed line) CL in the ($c_{H}$, $c_{H◻}$) parameter space, when all other Wilson coefficients are fixed to their SM values. The corresponding expected contours are shown by the inner and outer shaded regions. The SM prediction is indicated by the star, while the observed best-fit value is denoted by the black cross.

The acceptance times efficiency [in %] for the signal ggF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel. The other coupling modifiers affecting ggF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal ggF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ SLT channel. The other coupling modifiers affecting ggF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal ggF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ LTT channel. The other coupling modifiers affecting ggF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ SLT channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{\lambda}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ LTT channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{2V}$ in each analysis category for the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{2V}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ SLT channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

The acceptance times efficiency [in %] for the signal VBF $HH$ process as a function of the coupling modifier $\kappa_{2V}$ in each analysis category for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ LTT channel. The other coupling modifiers affecting VBF $HH$ production are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the fit to the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the fit to the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the fit to the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{\lambda}$ for the fit to the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the fit to the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Expected (dashed line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the fit to the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the fit to the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Observed (solid line) value of $-2\ln\Lambda$ as a function of $\kappa_{2V}$ for the fit to the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ channel, when all other coupling modifiers are fixed to their SM predictions.

Summary of the expected (blue) and observed (orange) one-dimensional constraints at 68% (solid error bars) and 95% (dashed error bars) CL on the coefficients of selected HEFT (top) and SMEFT (bottom) operators describing Higgs boson interactions affecting $HH$ production through gluon-gluon fusion. The results are obtained from one dimensional scans of the profile log-likelihood assuming that all other Wilson coefficients are fixed to their SM values. The contribution from VBF $HH$ production is neglected.

Local observed significance for signal discovery at different (mX, mS). The points show where the significance was evaluated. The band at mS = 125 GeV is not shown as those points are equivalent to those already probed in Phys. Rev. D 106 (2022) 052001.

Signal region yield for each of the simulated signal samples and interpolated signal points that were analyzed in the 1 b-jet Signal Region, normalized to a cross section of 1 fb. The signal region yields for one of the signals is found in Table 2 of the paper.

Signal region yield for each of the simulated signal samples and interpolated signal points that were analyzed in the 2 b-jet Signal Region, normalized to a cross section of 1 fb. The signal region yields for one of the signals is found in Table 2 of the paper.

Pre-fit background composition of the SR$3\ell$ Prod. The table shows the event yields as opposed to just the percentages of the relevant background processes.

Post-fit plot of $H_\text{T}(\text{jets})$ in the SR$2\ell$ Dec from a signal-blinded background-only fit.

Post-fit plot of $m(t_\text{SM}, b\text{-jet}_0)$ in the SR$2\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Dec from a signal-blinded background-only fit.

Post-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Prod from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Prod from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$e$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$\mu$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ $t\bar{t}V$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$e$ from the full fit to data.

Post-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$\mu$ from the full fit to data.

Post-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}W$ from the full fit to data.

Post-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}Z$ from the full fit to data.

Observed and expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hu)$ for different analyses and their statistical combination.

Observed and expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hc)$ for different analyses and their statistical combination.

Post-fit normalisation factors of free-floating background processes and the signal normalisation.

Post-fit predicted and observed yields in all $2\ell$SS signal and control regions. Pre-fit signal contributions for a signal cross section equivalent to $\mathcal{B}(t\to Hq)=0.1\,\%$ are given as well.

Post-fit predicted and observed yields in all $3\ell$ signal and control regions. Pre-fit signal contributions for a signal cross section equivalent to $\mathcal{B}(t\to Hq)=0.1\,\%$ are given as well.

Expected upper limits on $\mathcal{B}(t\to Hq)$ for the nominal fit and alternative fit configurations. One contains the full phase space but only considers statistical uncertainties. Two other configurations consider the full set of systematic uncertainties, but only encompass one final state.

Expected and observed upper limits on $\mathcal{B}(t\to Hq)$ and $|C_{u\phi}^{qt,tq}|$ for the full fit containing all systematic uncertainties.

Pre-fit plot of $H_\text{T}(\text{jets})$ in the SR$2\ell$ Dec from a signal-blinded background-only fit.

Pre-fit plot of $m(t_\text{SM}, b\text{-jet}_0)$ in the SR$2\ell$ Prod from a signal-blinded background-only fit.

Pre-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Dec from a signal-blinded background-only fit.

Pre-fit plot of $m(\ell_\text{OS},\ell_\text{SS,1})$ in the SR$3\ell$ Prod from a signal-blinded background-only fit.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Prod from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Dec from the full fit to data.

Post-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$2\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHc)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$2\ell$ Prod from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Dec from the full fit to data.

Pre-fit plot of $D_\text{NN}(tHu)$ in the SR$3\ell$ Prod from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$e$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ HF$\mu$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_1)$ in the CR$2\ell$ $t\bar{t}V$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$e$ from the full fit to data.

Pre-fit plot of $p_\text{T}(\ell_2)$ in the CR$3\ell$ HF$\mu$ from the full fit to data.

Pre-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}W$ from the full fit to data.

Pre-fit plot of $p_\text{T}(b\text{-jet}_0)$ in the CR$3\ell$ $t\bar{t}Z$ from the full fit to data.

Ranking of fit nuisance parameters according to their impact on the post-fit $tHu$ signal normalisation when fixed to $\pm1\sigma$

Ranking of fit nuisance parameters according to their impact on the post-fit $tHc$ signal normalisation when fixed to $\pm1\sigma$

Expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hu)$ for each individual final state and the full analysis.

Expected upper exclusion limits on the branching ratio $\mathcal{B}(t\to Hc)$ for each individual final state and the full analysis.

Distribution of the diphoton invariant mass in validation region VR2. The solid histograms are stacked to show the SM expectations after the 2&times;2D background estimation technique is applied. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. Statistical and systematic uncertainties are indicated by the shaded area. The lower panel of each plot shows the ratio of the data to the SM prediction for the respective bin. The first and last bins include the underflows and overflows respectively.

Distribution of the missing transverse momentum in validation region VR2. The solid histograms are stacked to show the SM expectations after the 2&times;2D background estimation technique is applied. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. Statistical and systematic uncertainties are indicated by the shaded area. The lower panel of each plot shows the ratio of the data to the SM prediction for the respective bin. The first and last bins include the underflows and overflows respectively.

Distribution of the diphoton invariant mass with all selections of the signal regions applied, except on m_{&gamma;&gamma;} itself, for signal region SR1h. The background estimation techniques described in the text are applied. The different backgrounds are stacked to add up to the total SM prediction in each bin. The predicted yields for signal benchmark models of varying &chi;&#771;⁰₁ mass are also overlaid (not stacked) assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; ) to equal 100%. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The sizes of the statistical and systematic uncertainties are indicated by the shaded areas. The lower panels show the ratio of the data to the SM prediction. Arrows indicate the borders of the signal region (|m_{&gamma;&gamma;}-125 GeV|&lt;5 GeV). The first and last bins include the underflows and overflows respectively.

Distribution of the diphoton invariant mass with all selections of the signal regions applied, except on m_{&gamma;&gamma;} itself, for signal region SR1Z. The background estimation techniques described in the text are applied. The different backgrounds are stacked to add up to the total SM prediction in each bin. The predicted yields for signal benchmark models of varying &chi;&#771;⁰₁ mass are also overlaid (not stacked) assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; ) to equal 50%. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The sizes of the statistical and systematic uncertainties are indicated by the shaded areas. The lower panels show the ratio of the data to the SM prediction. Arrows indicate the borders of the signal region (|m_{&gamma;&gamma;}-125 GeV|&lt;5 GeV). The first and last bins include the underflows and overflows respectively.

Distribution of the diphoton invariant mass with all selections of the signal regions applied, except on m_{&gamma;&gamma;} itself, for signal region SR2. The background estimation techniques described in the text are applied. The different backgrounds are stacked to add up to the total SM prediction in each bin. The predicted yields for signal benchmark models of varying &chi;&#771;⁰₁ mass are also overlaid (not stacked) assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; ) to equal 100%. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The sizes of the statistical and systematic uncertainties are indicated by the shaded areas. The lower panels show the ratio of the data to the SM prediction. Arrows indicate the borders of the signal region (|m_{&gamma;&gamma;}-125 GeV|&lt;5 GeV). The first and last bins include the underflows and overflows respectively.

Observed and expected limits on the pure higgsino cross-section at 95% CL assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; )=100% for different &chi;&#771;⁰₁ masses, obtained by a statistical combination of the three signal regions SR1h, SR1Z and SR2. The inner and outer bands indicate the 1&sigma; and 2&sigma; variation on the expected limit respectively.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Numbers of signal and background events in the signal regions. The respective background estimation techniques are applied. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The different backgrounds are stacked to add up to the total Standard Model prediction in each bin. The predicted yields for signal benchmark models of varying &chi;&#771;⁰₁ mass are also plotted (not stacked), assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; )=100% and a &chi;&#771;⁰₁ mass of 130 or 200 GeV. The statistical and systematic uncertainties are indicated by the shaded areas in the top plot. The bottom panel shows the statistical significance&nbsp;<a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2020-025/">[Ref]</a> of the difference between the SM prediction and the observed data in each region.

Observed 95% CL limits in pb on the pure higgsino cross-section, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Limits are obtained by a statistical combination of the three signal regions SR1h, SR1Z and SR2, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Acceptances for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Acceptances are provided separately for signal region SR1h, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Acceptances for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Acceptances are provided separately for signal region SR1Z, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Acceptances for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Acceptances are provided separately for signal region SR2, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Efficiencies for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Efficiencies are provided separately for signal region SR1h, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Efficiencies for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Efficiencies are provided separately for signal region SR1Z, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Efficiencies for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Efficiencies are provided separately for signal region SR2, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Observed and expected numbers of events in the three signal regions. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The table also includes model-independent 95% CL upper limits on the visible number of BSM events (S⁹⁵_obs), the number of BSM events given the expected number of background events (S⁹⁵_exp) and the visible BSM cross-section (&lang;&epsilon; &sigma;&rang;_obs⁹⁵), all calculated from pseudo-experiments. The discovery p-value (p₀) is also shown and its value is capped at 0.5 if the observed number of events is below the expected number of events.

Cutflows of two benchmark signal points assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; )=100% for all three discovery signal regions. The initial selection includes the leptons veto. Only statistical uncertainties are included. Expected yields are normalised to a luminosity of 139 fb^-1.

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.

Measured total $t\bar{t}\gamma$ production and decay fiducial inclusive cross-sections in both decay channels and in the combination.

Summary of the impact of the systematic uncertainties on the $t\bar{t}\gamma$ fiducial inclusive cross-section in the single-lepton and dilepton channels and their combination grouped into different categories. The quoted relative uncertainties are obtained by repeating the fit, fixing a set of nuisance parameters of the sources corresponding to each category to their post-fit values, and subtracting in quadrature the resulting uncertainty from the total uncertainty of the nominal fit. The total uncertainty is different from the sum in quadrature of the components due to correlations among nuisance parameters.

Absolute $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(\gamma,b)_{min}$. The last bin of the distribution includes the overflow events.

Normalised $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(\gamma,b)_{min}$. The last bin of the distribution includes the overflow events.

Absolute $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(\gamma,l)$. The last bin of the distribution includes the overflow events.

Normalised $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(\gamma,l)$. The last bin of the distribution includes the overflow events.

Absolute $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(l,j)_{min}$. The last bin of the distribution includes the overflow events.

Normalised $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(l,j)_{min}$. The last bin of the distribution includes the overflow events.

Absolute $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the dilepton channel as a function of the $\Delta R(\gamma,b)_{min}$. The last bin of the distribution includes the overflow events.

Normalised $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the dilepton channel as a function of the $\Delta R(\gamma,b)_{min}$. The last bin of the distribution includes the overflow events.

Absolute $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the dilepton channel as a function of the $\Delta R(\gamma,l)_{min}$. The last bin of the distribution includes the overflow events.

Normalised $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the dilepton channel as a function of the $\Delta R(\gamma,l)_{min}$. The last bin of the distribution includes the overflow events.

Absolute $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the dilepton channel as a function of the $\Delta R(l,j)_{min}$. The last bin of the distribution includes the overflow events.

Normalised $t\bar{t}\gamma$ production differential cross-sections measured in fiducial phase space in the dilepton channel as a function of the $\Delta R(l,j)_{min}$. The last bin of the distribution includes the overflow events.

Absolute $t\bar{t}\gamma$ production differential cross-sections measured in the combined fiducial phase space of the single-lepton and dilepton channels as a function of the photon $p_T$. The last bin of the distributions includes the overflow events.

Normalised $t\bar{t}\gamma$ production differential cross-sections measured in the combined fiducial phase space of the single-lepton and dilepton channels as a function of the photon $p_T$. The last bin of the distributions includes the overflow events.

Absolute $t\bar{t}\gamma$ production differential cross-sections measured in the combined fiducial phase space of the single-lepton and dilepton channels as a function of the photon $|\eta|$.

Normalised $t\bar{t}\gamma$ production differential cross-sections measured in the combined fiducial phase space of the single-lepton and dilepton channels as a function of the photon $|\eta|$.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $p_T(\gamma)$. The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $p_T(\gamma)$. The last bin of the distribution includes the overflow events.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $|\eta(\gamma)|$.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $|\eta(\gamma)|$.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $p_T(j_1)$. The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $p_T(j_1)$. The last bin of the distribution includes the overflow events.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(\gamma,b)_{min}$. The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(\gamma,b)_{min}$. The last bin of the distribution includes the overflow events.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(\gamma,l)$. The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(\gamma,l)$. The last bin of the distribution includes the overflow events.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(l,j)_{min}$. The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the single-lepton channel as a function of the $\Delta R(l,j)_{min}$. The last bin of the distribution includes the overflow events.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the dilepton channel as a function of the $|\Delta \eta(l,l)|$ . The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the dilepton channel as a function of the $|\Delta \eta(l,l)|$ . The last bin of the distribution includes the overflow events.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the dilepton channel as a function of the $\Delta \phi(l,l)$ . The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the dilepton channel as a function of the $\Delta \phi(l,l)$ . The last bin of the distribution includes the overflow events.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the dilepton channel as a function of the $p_T(l,l)$. The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the dilepton channel as a function of the $p_T(l,l)$. The last bin of the distribution includes the overflow events.

Absolute differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the dilepton channel as a function of the $\Delta R(\gamma,l_1)$. The last bin of the distribution includes the overflow events.

Normalised differential cross-section of the total $t\bar{t}\gamma$ production and decay measured in fiducial phase space in the dilepton channel as a function of the $\Delta R(\gamma,l_1)$. The last bin of the distribution includes the overflow events.