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.

Total covariance matrix for the unfolded Lund Jet Plane in the top jet selection.

Statistical covariance matrix for the unfolded Lund Jet Plane in the top jet selection.

Unfolded Lund Jet Plane in the W jet selection.

Unfolded Lund Jet Plane in the W jet selection, $2.11<\ln(1/z)<2.46$.

Unfolded Lund Jet Plane in the W jet selection, $1.60<\ln(R/\Delta R)<2.00$.

Total covariance matrix for the unfolded Lund Jet Plane in the W jet selection.

Statistical covariance matrix for the unfolded Lund Jet Plane in the W jet selection.

Mean transverse momentum $\langle p_{T} \rangle$ for $\Lambda$ and $K^{0}_{S}$ at mid-rapidity as a function of $N_{\text{part}}$ at 3 GeV.

Hadron 4$\pi$ yields per mean number of participants yield as a function of $N_{\text{part}}$.

Mid-rapidity yield ratios of $\Lambda/p$, $\Xi/\Lambda$, and $K^0_{S}/\Lambda$ at 3 GeV.

The transverse-momentum spectra of $\Lambda$ for different rapidities.

The transverse-momentum spectra of $\Lambda$ for different rapidities.

The transverse-momentum spectra of $\Lambda$ for different rapidities.

The transverse-momentum spectra of $\Lambda$ for different rapidities.

The transverse-momentum spectra of $\Lambda$ for different rapidities.

The transverse-momentum spectra of $\Lambda$ for different rapidities.

The transverse-momentum spectra of $K^{0}_{S}$ for different rapidities.

The transverse-momentum spectra of $K^{0}_{S}$ for different rapidities.

The transverse-momentum spectra of $K^{0}_{S}$ for different rapidities.

The transverse-momentum spectra of $K^{0}_{S}$ for different rapidities.

The transverse-momentum spectra of $K^{0}_{S}$ for different rapidities.

The transverse-momentum spectra of $K^{0}_{S}$ for different rapidities.

Hadron mass dependence of mean transverse momentum $\langle p_{T} \rangle$.

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.

95&percnt; CL expected and observed exclusion limits on the branching ratio of the Higgs boson to dark photons as a function of the &gamma;_d mass for the HAHM model.

95&percnt; CL expected and observed exclusion limits on the branching ratio of the Higgs boson to dark photons as a function of the &gamma;_d mass for the FRVZ model.

90&percnt; CL exclusion limit contours on the branching ratio of the Higgs boson to dark photons as functions of the &gamma;_d mass and kinetic mixing parameter &epsilon; for the HAHM model. These exclusions assume Higgs boson branching fractions to dark photons within a 0.01&percnt; and 10&percnt; range.

90&percnt; CL exclusion limit contours on the branching ratio of the Higgs boson to dark photons as functions of the &gamma;_d mass and kinetic mixing parameter &epsilon; for the FRVZ model. These exclusions assume Higgs boson branching fractions to dark photons within a 0.01&percnt; and 10&percnt; range.

The reconstruction efficiency for &mu;LJ objects produced in the decay of a &gamma;_d into a muon pair. The reconstruction efficiency is shown for different dark photon mass hypotheses generated according to the HAHM signal model. The efficiency is shown as a function of the dark photon p_T.

The reconstruction efficiency for &mu;LJ objects produced in the decay of a &gamma;_d into a muon pair. The reconstruction efficiency is shown for different dark photon mass hypotheses generated according to the FRVZ signal model. The efficiency is shown as a function of the dark photon p_T.

The reconstruction efficiency for &mu;LJ objects produced in the decay of a &gamma;_d into a muon pair. The reconstruction efficiency is shown for different dark photon mass hypotheses generated according to the HAHM signal model. The efficiency is shown as a function of the &Delta;R opening between the decay products of a dark photon.

The reconstruction efficiency for &mu;LJ objects produced in the decay of a &gamma;_d into a muon pair. The reconstruction efficiency is shown for different dark photon mass hypotheses generated according to the FRVZ signal model. The efficiency is shown as a function of the &Delta;R opening between the decay products of a dark photon.

The reconstruction efficiency for &mu;LJ objects produced in the decay of a &gamma;_d into a muon pair. The reconstruction efficiency is shown for a dark photon mass of 0.4 GeV, generated according to the HAHM and FRVZ signal models. The efficiency is shown as a function of the &gamma;_d p_T.

The reconstruction efficiency for &mu;LJ objects produced in the decay of a &gamma;_d into a muon pair. The reconstruction efficiency is shown for a dark photon mass of 0.4 GeV, generated according to the HAHM and FRVZ signal models. The efficiency is shown as a function of the &Delta;R opening between the decay products of a dark photon.

The reconstruction efficiency for eLJ objects produced in the decay of a &gamma;_d into an electron pair. The reconstruction efficiency is shown for different dark photon mass hypotheses generated according to the HAHM signal model. The efficiency is shown as a function of the dark photon p_T.

The reconstruction efficiency for eLJ objects produced in the decay of a &gamma;_d into an electron pair. The reconstruction efficiency is shown for different dark photon mass hypotheses generated according to the FRVZ signal model. The efficiency is shown as a function of the dark photon p_T.

The reconstruction efficiency for eLJ objects produced in the decay of a &gamma;_d into a pair of electrons. The reconstruction efficiency is shown for different dark photon mass hypotheses generated according to the HAHM signal model. The efficiency is shown as a function of the &Delta;R opening between the decay products of a dark photon. The decrease in efficiency, visible at 0.1, is due to the transition between the region of the phase space where eLJs are identified from merged EM showers with two tracks, to the one where eLJs are obtained by the clustering of two resolved electrons.

The reconstruction efficiency for eLJ objects produced in the decay of a &gamma;_d into a pair of electrons. The reconstruction efficiency is shown for different dark photon mass hypotheses generated according to the FRVZ signal model. The efficiency is shown as a function of the &Delta;R opening between the decay products of a dark photon. The decrease in efficiency, visible at 0.1, is due to the transition between the region of the phase space where eLJs are identified from merged EM showers with two tracks, to the one where eLJs are obtained by the clustering of two resolved electrons.

The reconstruction efficiency for eLJ objects produced in the decay of a &gamma;_d into a pair of electrons. The reconstruction efficiency is shown for a dark photon mass of 0.1 GeV, generated according to the HAHM and FRVZ signal models. The efficiency is shown as a function of the &gamma;_d p_T.

The reconstruction efficiency for eLJ objects produced in the decay of a &gamma;_d into a pair of electrons. The reconstruction efficiency is shown for a dark photon mass of 0.1 GeV, generated according to the HAHM and FRVZ signal models. The efficiency is shown as a function of the &Delta;R opening between the decay products of a dark photon.

95&percnt; CL expected and observed exclusion limits on the branching ratio of the Higgs boson to dark photons as a function of the &gamma;_d mass for the HAHM model, obtained from the &mu;LJ&ndash;&mu;LJ signal region.

95&percnt; CL expected and observed exclusion limits on the branching ratio of the Higgs boson to dark photons as a function of the &gamma;_d mass for the FRVZ model, obtained from the &mu;LJ&ndash;&mu;LJ signal region.

95&percnt; CL expected and observed exclusion limits on the branching ratio of the Higgs boson to dark photons as a function of the &gamma;_d mass for the HAHM model, obtained from the &mu;LJ&ndash;eLJ signal region.

95&percnt; CL expected and observed exclusion limits on the branching ratio of the Higgs boson to dark photons as a function of the &gamma;_d mass for the FRVZ model, obtained from the &mu;LJ&ndash;eLJ signal region.

Acceptance times efficiencies (A&times;&varepsilon;) as a function of the &gamma;_d mass for the HAHM process for the three signal regions of the analysis, eLJ-eLJ, eLJ-&mu;LJ and &mu;LJ-&mu;LJ. The decrease of the A&times;&varepsilon; is expected for increasing masses of the &gamma;_d, as its decay products become resolved and are no longer reconstructed as LJs. The local minimum around m_{&gamma;_d}=2 GeV observed for the eLJ-&mu;LJ distribution is expected, as the reconstruction efficiency for eLJs with merged EM showers decreases before the reconstruction of eLJ with two resolved electrons becomes efficient.

Acceptance times efficiencies (A&times;&varepsilon;) as a function of the &gamma;_d mass for the FRVZ process for the three signal regions of the analysis, eLJ-eLJ, eLJ-&mu;LJ and &mu;LJ-&mu;LJ. The decrease of the A&times;&varepsilon; is expected for increasing masses of the &gamma;_d, as its decay products become resolved and are no longer reconstructed as LJs. The local minimum around m_{&gamma;_d}=2 GeV observed for the eLJ-&mu;LJ distribution is expected, as the reconstruction efficiency for eLJs with merged EM showers decreases before the reconstruction of eLJ with two resolved electrons becomes efficient.

The transverse-mass dependence of the correlation-strength parameter $\lambda$ in 30-40% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the correlation-strength parameter $\lambda$ in 40-50% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the correlation-strength parameter $\lambda$ in 50-60% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-scale parameter $R$ in 0-10% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-scale parameter $R$ in 10-20% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-scale parameter $R$ in 20-30% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-scale parameter $R$ in 30-40% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-scale parameter $R$ in 40-50% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-scale parameter $R$ in 50-60% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-index of stability parameter $\alpha$ in 0-10% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-index of stability parameter $\alpha$ in 10-20% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-index of stability parameter $\alpha$ in 20-30% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-index of stability parameter $\alpha$ in 30-40% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-index of stability parameter $\alpha$ in 40-50% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the Lévy-index of stability parameter $\alpha$ in 50-60% centrality bin obtained from Lévy fits with Eq. (9).

The transverse-mass dependence of the normalized correlation-strength parameter $\lambda/\lambda_{\rm max}$ for 0-10% centrality intervals. For each centrality bin the data are fitted with the Gaussian function of Eq. (15). This parameterization can describe the data and is discussed in detail in Section VI B. The fit parameters with the statistic and systematic uncertainties are shown in Fig. 7.

The transverse-mass dependence of the normalized correlation-strength parameter $\lambda/\lambda_{\rm max}$ for 10-20% centrality intervals. For each centrality bin the data are fitted with the Gaussian function of Eq. (15). This parameterization can describe the data and is discussed in detail in Section VI B. The fit parameters with the statistic and systematic uncertainties are shown in Fig. 7.

The transverse-mass dependence of the normalized correlation-strength parameter $\lambda/\lambda_{\rm max}$ for 20-30% centrality intervals. For each centrality bin the data are fitted with the Gaussian function of Eq. (15). This parameterization can describe the data and is discussed in detail in Section VI B. The fit parameters with the statistic and systematic uncertainties are shown in Fig. 7.

The transverse-mass dependence of the normalized correlation-strength parameter $\lambda/\lambda_{\rm max}$ for 30-40% centrality intervals. For each centrality bin the data are fitted with the Gaussian function of Eq. (15). This parameterization can describe the data and is discussed in detail in Section VI B. The fit parameters with the statistic and systematic uncertainties are shown in Fig. 7.

The transverse-mass dependence of the normalized correlation-strength parameter $\lambda/\lambda_{\rm max}$ for 40-50% centrality interval. For each centrality bin the data are fitted with the Gaussian function of Eq. (15). This parameterization can describe the data and is discussed in detail in Section VI B. The fit parameters with the statistic and systematic uncertainties are shown in Fig. 7.

The transverse-mass dependence of the normalized correlation-strength parameter $\lambda/\lambda_{\rm max}$ for 50-60% centrality interval. For each centrality bin the data are fitted with the Gaussian function of Eq. (15). This parameterization can describe the data and is discussed in detail in Section VI B. The fit parameters with the statistic and systematic uncertainties are shown in Fig. 7.

The transverse-mass dependence of the inverse square of the Lévy-scale parameter $R$, shown in 0-10% centrality bin. The values and uncertainties of the A and B linear fit parameters with the statistic and systematic uncertainties are shown in Fig. 8.

The transverse-mass dependence of the inverse square of the Lévy-scale parameter $R$, shown in 10-20% centrality bin. The values and uncertainties of the A and B linear fit parameters with the statistic and systematic uncertainties are shown in Fig. 8.

The transverse-mass dependence of the inverse square of the Lévy-scale parameter $R$, shown in 20-30% centrality bin. The values and uncertainties of the A and B linear fit parameters with the statistic and systematic uncertainties are shown in Fig. 8.

The transverse-mass dependence of the inverse square of the Lévy-scale parameter $R$, shown in 30-40% centrality bin. The values and uncertainties of the A and B linear fit parameters with the statistic and systematic uncertainties are shown in Fig. 8.

The transverse-mass dependence of the inverse square of the Lévy-scale parameter $R$, shown in 40-50% centrality bin. The values and uncertainties of the A and B linear fit parameters with the statistic and systematic uncertainties are shown in Fig. 8.

The transverse-mass dependence of the inverse square of the Lévy-scale parameter $R$, shown in 50-60% centrality bin. The values and uncertainties of the A and B linear fit parameters with the statistic and systematic uncertainties are shown in Fig. 8.

The transverse-mass dependence of the $\widehat{R}$ parameter, shown in 0-10% centrality bin. The values and uncertainties of the linear fit parameters $\widehat{A}$ and $\widehat{B}$ with the statistic and systematic uncertainties are shown in Fig. 9

The transverse-mass dependence of the $\widehat{R}$ parameter, shown in 10-20% centrality bin. The values and uncertainties of the linear fit parameters $\widehat{A}$ and $\widehat{B}$ with the statistic and systematic uncertainties are shown in Fig. 9

The transverse-mass dependence of the $\widehat{R}$ parameter, shown in 20-30% centrality bin. The values and uncertainties of the linear fit parameters $\widehat{A}$ and $\widehat{B}$ with the statistic and systematic uncertainties are shown in Fig. 9

The transverse-mass dependence of the $\widehat{R}$ parameter, shown in 30-40% centrality bin. The values and uncertainties of the linear fit parameters $\widehat{A}$ and $\widehat{B}$ with the statistic and systematic uncertainties are shown in Fig. 9

The transverse-mass dependence of the $\widehat{R}$ parameter, shown in 40-50% centrality bin. The values and uncertainties of the linear fit parameters $\widehat{A}$ and $\widehat{B}$ with the statistic and systematic uncertainties are shown in Fig. 9

The transverse-mass dependence of the $\widehat{R}$ parameter, shown in 50-60% centrality bin. The values and uncertainties of the linear fit parameters $\widehat{A}$ and $\widehat{B}$ with the statistic and systematic uncertainties are shown in Fig. 9

The $H$ parameter that characterize the magnitude of the $\lambda/\lambda_{max}(m_T)$ and is defined in Eq. (15)., shown as functions of $N_{part}$.

The $\sigma$ parameter that characterize the width of the $\lambda/\lambda_{max}(m_T)$ and is defined in Eq. (15)., shown as functions of $N_{part}$.

The parameter $A$ of the affine linear fit to the inverse square of the Lévy-scale parameter that are defined in Eq.(12) as function of $N_{part}$

The parameter $B$ of the affine linear fit to the inverse square of the Lévy-scale parameter that are defined in Eq.(12) as function of $N_{part}$

The parameter $\widehat{A}$ of the affine linear fit to the inverse of the $\widehat{R}$ parameter that are defined in Eq.(14) as function of $N_{part}$.

The parameter $\widehat{B}$ of the affine linear fit to the inverse of the $\widehat{R}$ parameter that are defined in Eq.(14) as function of $N_{part}$.

The centrality dependence of $\alpha_0$, the $m_T$-averaged value of $\alpha$ as a function of $N_{part}$.

The Lévy scale parameter $R$ as a function of $N^{1/3}_{part}$ at $m_T=0.326$ GeV.

The Lévy scale parameter $R$ as a function of $N^{1/3}_{part}$ at $m_T=0.438$ GeV.

The Lévy scale parameter $R$ as a function of $N^{1/3}_{part}$ at $m_T=0.553$ GeV.

The Lévy scale parameter $R$ as a function of $N^{1/3}_{part}$ at $m_T=0.670$ GeV.

The Lévy scale parameter $R$ as a function of $N^{1/3}_{part}$ at $m_T=0.787$ GeV.

Monte Carlo calcuations with no mass drop assumed in 0-10%.

Monte Carlo calcuations with no mass drop assumed in 10-20%.

Monte Carlo calcuations with no mass drop assumed in 20-30%.

Monte Carlo calcuations with no mass drop assumed in 30-40%.

Monte Carlo calcuations with no mass drop assumed in 40-50%.

Monte Carlo calcuations with no mass drop assumed in 50-60%.

Monte Carlo calcuations with the best mass drop assumed in 0-10%.

Monte Carlo calcuations with the best mass drop assumed in 10-20%.

Monte Carlo calcuations with the best mass drop assumed in 20-30%.

Monte Carlo calcuations with the best mass drop assumed in 30-40%.

Monte Carlo calcuations with the best mass drop assumed in 40-50%.

Monte Carlo calcuations with the best mass drop assumed in 50-60%.

The best values of $B^{-1}_{\eta\prime}$ spectrum of the $\eta\prime$ condensate in the six centrality classes.

The centrality dependence of the best values of the in-medium mass of the $m^{*}_{\eta\prime}$.

The CL maps of the comparison of the MC to data in 0-10% (see Fig. 11).

The CL maps of the comparison of the MC to data in 10-20% (see Fig. 11).

The CL maps of the comparison of the MC to data in 20-30% (see Fig. 11).

The CL maps of the comparison of the MC to data in 30-40% (see Fig. 11).

The CL maps of the comparison of the MC to data in 40-50% (see Fig. 11).

The CL maps of the comparison of the MC to data in 50-60% (see Fig. 11).

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow vs pseudorapidity.

Directed flow as a function of (pseudorapidity-y_beam) for Au+Au collisions at sqrt{s_NN} = 19.6 GeV for 0-40% centrality.

Directed flow as a function of (pseudorapidity-y_beam) for Au+Au collisions at sqrt{s_NN} = 27 GeV for 0-40% centrality.

Directed flow as a function of pseudorapidity for Au+Au collisions at sqrt{s_NN} = 19.6 GeV for 10-40% centrality.

Directed flow as a function of pseudorapidity for Au+Au collisions at sqrt{s_NN} = 27 GeV for 10-40% centrality.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 0%-20% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side a function of Deltaphi 20%-40% collisions. Systematic uncertainties for background subtraction and global scale uncertainties are given.

Differential away-side $\Delta_{AA}$ for ${\pi/2<\Delta\phi<\pi}$

Differential away-side $\Delta_{AA}$ for ${\pi/2<\Delta\phi<\pi}$

Differential away-side $\Delta_{AA}$ for ${\pi/2<\Delta\phi<\pi}$