Correlation matrix for the correlated systematic uncertainties of the forward rapidity $\eta$ meson cross section.

Correlation matrix for the correlated systematic uncertainties of the central rapidity $\eta$ meson cross section.

The invariant yields of $\phi$ and $\omega+\rho$ mesons as a function of $\langle N_{\rm part}\rangle$ in Au+Au collisions. The systematic uncertainties of type-A (uncorrelated) are combined with statistical uncertainties in quadrature and are labeled as stat. Type-B (correlated) systematic uncertainties are listed as sys.

$R_{AA}$ of $\phi$ meson as a function of $p_T$ for in Au+Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV, compared with Cu+Au collisions at $1.2<|y|<2.2$. The systematic uncertainties of type-A (uncorrelated) are combined with statistical uncertainties in quadrature and are labeled as stat. Type-B (correlated) systematic uncertainties are listed as sys.

$R_{AA}$ of $\phi$ meson as a function of $\langle N_{\rm part}\rangle$ for $1.2<|y|<2.2$ in Au+Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. The systematic uncertainties of type-A (uncorrelated) are combined with statistical uncertainties in quadrature and are labeled as stat. Type-B (correlated) systematic uncertainties are listed as sys.

Observed profile likelihood scans of $\kappa_\lambda$. The scans are performed by varying only the coupling modifier of interest, while all other relevant coupling modifiers are fixed to unity.

Expected profile likelihood scans of $\kappa_\lambda$. The scans are performed by varying only the coupling modifier of interest, while all other relevant coupling modifiers are fixed to unity.

Observed profile likelihood scans of $\kappa_{2V}$. The scans are performed by varying only the coupling modifier of interest, while all other relevant coupling modifiers are fixed to unity.

Expected profile likelihood scans of $\kappa_{2V}$. The scans are performed by varying only the coupling modifier of interest, while all other relevant coupling modifiers are fixed to unity.

Confidence level contours at 68% (solid line) and 95% (dashed line) in the $(\kappa_\lambda, \kappa_{2V})$ parameter space, when all other coupling modifiers are fixed to their SM predictions. The corresponding expected contours are shown by the inner and outer shaded regions The SM prediction is indicated by the star, while the best-fit value is denoted by the cross.

Confidence level contours at 68% (solid line) and 95% (dashed line) in the $(\kappa_\lambda, \kappa_{2V})$ parameter space, when all other coupling modifiers are fixed to their SM predictions. The corresponding expected contours are shown by the inner and outer shaded regions The SM prediction is indicated by the star, while the best-fit value is denoted by the cross.

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 2. 3he lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the high-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 2. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed $m_{\gamma\gamma}$ distributions for the low-mass categories in Run 3. The lines show the fit results for the continuum background only (light dotted), adding the single Higgs boson background (black dotted) and the full fit (solid).

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

Observed and expected profiled likelihood scans on $\kappa_\lambda$ for the low-mass (LM) and high-mass (HM) regions separately and for their combination. The scan is performed by floating the indicated parameter of interest while fixing all the others to their SM value.

The expected number of events (estimated by using simulation) from the SM HH signals and single Higgs boson production, and the expected number of events from the continuum background, evaluated in the 120 GeV < $m_{\gamma\gamma}$ < 130 GeV window in Run 3 for the different low-mass (LM) and high-mass (HM) categories. For comparison, the number of data events is also shown. The uncertainties in the HH signals and single Higgs boson backgrounds include the systematic uncertainties discussed in Section 6. Asymmetric uncertainties arise primarily from the theory calculation of the SM ggF HH cross section and the large uncertainty in the yield of single Higgs bosons produced in ggF events in association with heavy-flavour jets. The uncertainty in the continuum background is given by the sum in quadrature of the statistical uncertainty from the fit to the data and the spurious signal uncertainty.

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-ej and (c, d) SR-2L-ej of the e+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_de) = (2.0&nbsp;TeV, 1.0) and S&#771;₁ (m, y_de) = (3.0&nbsp;TeV, 1.0), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;j and (c, d) SR-2L-&mu;j of the &mu;+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_s&mu;) = (2.0&nbsp;TeV, 1.5) and S&#771;₁ (m, y_s&mu;) = (3.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;j and (c, d) SR-2L-&mu;j of the &mu;+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_s&mu;) = (2.0&nbsp;TeV, 1.5) and S&#771;₁ (m, y_s&mu;) = (3.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;j and (c, d) SR-2L-&mu;j of the &mu;+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_s&mu;) = (2.0&nbsp;TeV, 1.5) and S&#771;₁ (m, y_s&mu;) = (3.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;j and (c, d) SR-2L-&mu;j of the &mu;+light-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_s&mu;) = (2.0&nbsp;TeV, 1.5) and S&#771;₁ (m, y_s&mu;) = (3.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-eb and (c, d) SR-2L-eb of the e+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_be) = (1.5&nbsp;TeV, 2.5) and S&#771;₁ (m, y_be) = (2.0&nbsp;TeV, 2.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-eb and (c, d) SR-2L-eb of the e+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_be) = (1.5&nbsp;TeV, 2.5) and S&#771;₁ (m, y_be) = (2.0&nbsp;TeV, 2.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-eb and (c, d) SR-2L-eb of the e+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_be) = (1.5&nbsp;TeV, 2.5) and S&#771;₁ (m, y_be) = (2.0&nbsp;TeV, 2.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-eb and (c, d) SR-2L-eb of the e+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_be) = (1.5&nbsp;TeV, 2.5) and S&#771;₁ (m, y_be) = (2.0&nbsp;TeV, 2.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;b and (c, d) SR-2L-&mu;b of the &mu;+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_b&mu;) = (1.5&nbsp;TeV, 1.5) and S&#771;₁ (m, y_b&mu;) = (2.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;b and (c, d) SR-2L-&mu;b of the &mu;+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_b&mu;) = (1.5&nbsp;TeV, 1.5) and S&#771;₁ (m, y_b&mu;) = (2.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;b and (c, d) SR-2L-&mu;b of the &mu;+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_b&mu;) = (1.5&nbsp;TeV, 1.5) and S&#771;₁ (m, y_b&mu;) = (2.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Data (dots) and post-fit SM distribution (histograms) of m_&#8467;j in (a, b) SR-1L-&mu;b and (c, d) SR-2L-&mu;b of the &mu;+b-jet channel obtained by a CR+SR background-only fit for Run&nbsp;2 and Run&nbsp;3, respectively. The lower panel shows the ratio of observed data to the total post- and pre-fit SM prediction. The last bin includes the overflow. Uncertainties in the background estimates include both the statistical and systematic uncertainties, with correlations between uncertainties taken into account. The dashed lines show the predicted yields for two benchmark signal models corresponding to S&#771;₁ (m, y_b&mu;) = (1.5&nbsp;TeV, 1.5) and S&#771;₁ (m, y_b&mu;) = (2.0&nbsp;TeV, 1.5), respectively. Note: the values in the table are normalized by the width of corresponding bin

Exclusion limits for minimal models of S&#771;₁ production with only y_de being non-zero obtained from a simultaneous fit to SR-1L and SR-2L of the e+light-jet channel combining Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (red solid lines) and expected (black dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y_de plane. The observed exclusion should be interpreted as the region above the red line. The yellow inner (green outer) shaded band around the expected limits corresponds to the &plusmn;1 &sigma; (&plusmn;2 &sigma;) variations of the expected limit, accounting for all uncertainties. The observed limit obtained from ATLAS searches for LQ pair production is also shown as dark blue line [13]. Constraints from weak charge measurements of protons and nuclei on y_de couplings derived by Ref. [10] are shown as light magenta line.

Exclusion limits for minimal models of S&#771;₁ production with only y_s&mu; being non-zero obtained from a simultaneous fit to SR-1L and SR-2L of the &mu;+light-jet channel combining Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (red solid lines) and expected (black dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y_s&mu; plane. The observed exclusion should be interpreted as the region above the red line. The yellow inner (green outer) shaded band around the expected limits corresponds to the &plusmn;1 &sigma; (&plusmn;2 &sigma;) variations of the expected limit, accounting for all uncertainties. The observed limit obtained from ATLAS searches for LQ pair production is also shown as dark blue line [13].

Exclusion limits for minimal models of S&#771;₁ production with only y_be being non-zero obtained from a simultaneous fit to SR-1L and SR-2L of the e+b-jet channel combining Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (red solid lines) and expected (black dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y_be plane. The observed exclusion should be interpreted as the region above the red line. The yellow inner (green outer) shaded band around the expected limits corresponds to the &plusmn;1 &sigma; (&plusmn;2 &sigma;) variations of the expected limit, accounting for all uncertainties. The observed limit obtained from ATLAS searches for LQ pair production is also shown as dark blue line [13].

Exclusion limits for minimal models of S&#771;₁ production with only y_b&mu; being non-zero obtained from a simultaneous fit to SR-1L and SR-2L of the &mu;+b-jet channel combining Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (red solid lines) and expected (black dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y_b&mu; plane. The observed exclusion should be interpreted as the region above the red line. The yellow inner (green outer) shaded band around the expected limits corresponds to the &plusmn;1 &sigma; (&plusmn;2 &sigma;) variations of the expected limit, accounting for all uncertainties. The observed limit obtained from ATLAS searches for LQ pair production is also shown as dark blue line [13].

Exclusion limits for minimal models of S&#771;₁ production obtained from a fit to SR-1L, SR-2L and their combination of (a) the e+light-jet, (b) the &mu;+light-jet, (c) the e+b-jet and (d) the &mu;+b-jet channel using Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (solid lines) and expected (dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y plane. The observed exclusion regions should be interpreted as the region above the solid lines. Constraints from weak charge measurements of protons and nuclei&nbsp; and ATLAS searches for LQ pair production&nbsp; are shown as light magenta and dark blue areas, respectively.

Exclusion limits for minimal models of S&#771;₁ production obtained from a fit to SR-1L, SR-2L and their combination of (a) the e+light-jet, (b) the &mu;+light-jet, (c) the e+b-jet and (d) the &mu;+b-jet channel using Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (solid lines) and expected (dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y plane. The observed exclusion regions should be interpreted as the region above the solid lines. Constraints from weak charge measurements of protons and nuclei&nbsp; and ATLAS searches for LQ pair production&nbsp; are shown as light magenta and dark blue areas, respectively.

Exclusion limits for minimal models of S&#771;₁ production obtained from a fit to SR-1L, SR-2L and their combination of (a) the e+light-jet, (b) the &mu;+light-jet, (c) the e+b-jet and (d) the &mu;+b-jet channel using Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (solid lines) and expected (dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y plane. The observed exclusion regions should be interpreted as the region above the solid lines. Constraints from weak charge measurements of protons and nuclei&nbsp; and ATLAS searches for LQ pair production&nbsp; are shown as light magenta and dark blue areas, respectively.

Exclusion limits for minimal models of S&#771;₁ production obtained from a fit to SR-1L, SR-2L and their combination of (a) the e+light-jet, (b) the &mu;+light-jet, (c) the e+b-jet and (d) the &mu;+b-jet channel using Run-2 and Run-3 data. All limits are computed at 95&percnt; CL and the observed (solid lines) and expected (dashed lines) exclusion limits are shown in the m(S&#771;₁) &ndash; y plane. The observed exclusion regions should be interpreted as the region above the solid lines. Constraints from weak charge measurements of protons and nuclei&nbsp; and ATLAS searches for LQ pair production&nbsp; are shown as light magenta and dark blue areas, respectively.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$ej$ and (b) Run 2 SR-2L-$ej$ and (c) Run 3 SR-1-$ej$ and (d) Run 3 SR-2-$ej$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu j$ and (b) Run 2 SR-2L-$\mu j$ and (c) Run 3 SR-1L-$\mu j$ and (d) Run 3 SR-2L-$\mu j$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP of $\tilde{S}$ in (a) Run 2 SR-1L-$eb$ and (b) Run 2 SR-2L-$eb$ and (c) Run 3 SR-1L-$eb$ and (d) Run 3 SR-2L-$eb$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for resonant production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Values of the signal acceptance multiplied by the selection efficiency ($A\times\epsilon$) for DY+SP production of $\tilde{S}$ in (a) Run 2 SR-1L-$\mu b$ and (b) Run 2 SR-2L-$\mu b$ and (c) Run 3 SR-1L-$\mu b$ and (d) Run 3 SR-2L-$\mu b$. The $A \times \epsilon$ values are calculated as the number of events of reconstructed-level signal simulation divided by the total cross-section of the signal process times the integrated luminosity.

Event selection cutflows of SR-1L-$eb$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{be} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-2L-$eb$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{be} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-1L-$ej$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{de} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-2L-$ej$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{de} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-1L-$\mu b$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{b\mu} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-2L-$\mu b$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{b\mu} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-1L-$\mu j$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{s\mu} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Event selection cutflows of SR-2L-$\mu j$ for the signal sample with $m(\tilde{S}) = 2$ TeV and $y_{s\mu} = 1.0$ individually for resonant production (RP) and combined Drell-Yan plus single production (DY+SP). The first number in the column for each mass points corresponds to the signal event yield after applying the associated cut while the second number in parentheses represents the efficiency with respect to the previous cut.

Unfolded $R(\rho_{s})$ observable in the 2-to-3 measurement (normalized and divided by bin width). The parton level is defined with two stable top-quarks and a jet with $p_{T}>50$ GeV and $|\eta|<2.5$.

Covariance matrix for statistical effects of the measured $R(\rho_{s})$ observable after unfolding, for the 2-to-3 measurement (normalized)

Covariance matrix for statistical and systematic effects of the measured $R(\rho_{s})$ observable after unfolding, for the 2-to-3 measurement (normalized)

Impact of systematic uncertainties on the 2-to-3 unfolded observable. Values are given in percentage of bin content.

Central value and breakdown of the uncertainties affecting the top-quark pole mass extraction from the 2-to-3 unfolded observable.

Unfolded number of events in the 2-to-7measurement (not normalized). The parton level is defined with two neutrinos, one electron and one muon of opposite electric charges, two $b$-jets and an additional jet (extrajet). The four-momentum of the sum of neutrinos has transverse component larger than 30 GeV. The $p_{T}$-leading lepton has $p_{T}>28$ GeV, while the sub-leading has $p_{T}>20$ GeV. The two $b$-jets with have $p_{T}>30$ GeV and the extrajet has $p^\text{extrajet}_{T}>60$ GeV. All the leptons and jets are separated by $\Delta R >0.4$ and have $|\eta|<2.5$.

Covariance matrix for statistical effects of the measured number of events after unfolding, for the 2-to-7 measurement (not normalized)

Covariance matrix for statistical and systematic effects of the measured number of events after unfolding, for the 2-to-7 measurement (not normalized)

Unfolded $R(\rho_{s})$ observable in the 2-to-7 measurement (normalized and divided by bin width). The parton level is defined with two neutrinos, one electron and one muon of opposite electric charges, two $b$-jets and an additional jet (extrajet). The four-momentum of the sum of neutrinos has transverse component larger than 30 GeV. The $p_{T}$-leading lepton has $p_{T}>28$ GeV, while the sub-leading has $p_{T}>20$ GeV. The two $b$-jets with have $p_{T}>30$ GeV and the extrajet has $p^\text{extrajet}_{T}>60$ GeV. All the leptons and jets are separated by $\Delta R >0.4$ and have $|\eta|<2.5$.

Covariance matrix for statistical effects of the measured $R(\rho_{s})$ observable after unfolding, for the 2-to-7 measurement (normalized)

Covariance matrix for statistical and systematic effects of the measured $R(\rho_{s})$ observable after unfolding, for the 2-to-7 measurement (normalized)

Impact of systematic uncertainties on the 2-to-7 unfolded observable. Values are given in percentage of bin content.

Central value and breakdown of the uncertainties affecting the top-quark pole mass extraction from the 2-to-7 unfolded observable.

Correlations (stat.+syst.) between the $d\Gamma_i/d w$ bins for the individual $B \rightarrow D \ell \nu$ modes (40x40). Element indices 0-39 are organized as: - Indices 0-9: $B^+ \to D^0 e^+ \nu_e$ in $w$ bins 0-9 - Indices 10-19: $B^+ \to D^0 \mu^+ \nu_\mu$ in $w$ bins 0-9 - Indices 20-29: $B^0 \to D^- e^+ \nu_e$ in $w$ bins 0-9 - Indices 30-39: $B^0 \to D^- \mu^+ \nu_\mu$ in $w$ bins 0-9 where $w$ bins 0-9 correspond to: 0: [1.00, 1.06], 1: [1.06, 1.12], 2: [1.12, 1.18], 3: [1.18, 1.24], 4: [1.24, 1.30], 5: [1.30, 1.36], 6: [1.36, 1.42], 7: [1.42, 1.48], 8: [1.48, 1.54], 9: [1.54, 1.59]

Distributions of the VLQ-candidate mass, m_VLQ, in the (a&ndash;c) SRs, (d&ndash;f) W+jets CRs and (g&ndash;i) tt&#772; CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a&ndash;c) SRs, (d&ndash;f) W+jets CRs and (g&ndash;i) tt&#772; CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a&ndash;c) SRs, (d&ndash;f) W+jets CRs and (g&ndash;i) tt&#772; CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a&ndash;c) SRs, (d&ndash;f) W+jets CRs and (g&ndash;i) tt&#772; CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a&ndash;c) SRs, (d&ndash;f) W+jets CRs and (g&ndash;i) tt&#772; CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the (a&ndash;c) SRs, (d&ndash;f) W+jets CRs and (g&ndash;i) tt&#772; CRs after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or that contain two W/Z bosons. The last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt&#772; VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt&#772; VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt&#772; VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt&#772; VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt&#772; VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

Distributions of the VLQ-candidate mass, m_VLQ, in the W+jets VRs (a-f) and tt&#772; VRs (g-i) after the fit to the background-only hypothesis. The columns correspond from left to right to the low-, middle-, and high-p_T^W bins in each region. Other includes remaining backgrounds from top quarks or containing combinations having two W/Z bosons. Last bin includes overflow. Note: the 'Data' values in the table are normalized by the width of the bin to correspond to the number of events per 100 GeV

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

$\xi$ distributions for different jet $p_T$ bins.

$\xi$ distributions for different jet $p_T$ bins.

The $j_T/p_{T}^{\rm jet}$ distributions for different jet $p_T$ bins.

The $j_T/p_{T}^{\rm jet}$ distributions for different jet $p_T$ bins.

$r$ distributions for different jet $p_T$ bins.

Normalized $p_{\mathrm{T}}$-spectrum ratio as a function as a function of centrality in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}} \in$ $0.5 \leq |\eta|\leq 0.8$.

Normalized $p_{\mathrm{T}}$-spectrum ratio as a function as a function of centrality in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{tracklets}} \in$ $0.5 \leq |\eta|\leq 0.8$.

Normalized $p_{\mathrm{T}}$-spectrum ratio as a function as a function of centrality in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}} \in$ $-3.7<\eta<-1.7$ and $2.8 < \eta <5.1$.

Event fraction distribution as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}} \in -3.7<\eta<-1.7$ and $2.8 < \eta <5.1$.

Event fraction distribution as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}} \in |\eta|\leq 0.8$.

Event fraction distribution as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}} \in |\eta|\leq 0.8$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $|\eta| \leq 0.8$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $0.5 \leq |\eta| \leq 0.8$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}}$ $\in$ $|\eta| \leq 0.8$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}}$ $\in$ $0.5 \leq |\eta| \leq 0.8$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{tracklets}}$ $\in$ $|\eta| \leq 0.8$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{tracklets}}$ $\in$ $0.5 \leq |\eta| \leq 0.8$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{tracklets}}$ $\in$ $0.3 \leq |\eta| \leq 0.6$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{tracklets}}$ $\in$ $0.7 \leq |\eta| \leq 1$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $-3.7<\eta<-1.7$ and $2.8 < \eta <5.1$.

Extracted $c_{\mathrm{s}}^{2}$ as a function of the minimum pseudorapidity gap between the centrality estimation and transverse-momentum spectra measurement in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$.

$k_{2}/k_{2}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $|\eta| \leq 0.8$.

$k_{2}/k_{2}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}}$ $\in$ $|\eta| \leq 0.8$.

$k_{2}/k_{2}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $-3.7<\eta<-1.7$ and $2.8 < \eta <5.1$.

$k_{3}/k_{3}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $|\eta| \leq 0.8$.

$k_{3}/k_{3}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}}$ $\in$ $|\eta| \leq 0.8$.

$k_{3}/k_{3}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $-3.7<\eta<-1.7$ and $2.8 < \eta <5.1$.

$\gamma_{\langle [p_{\mathrm{T}}] \rangle}/\gamma_{\langle [p_{\mathrm{T}}] \rangle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $|\eta| \leq 0.8$.

$\gamma_{\langle [p_{\mathrm{T}}] \rangle}/\gamma_{\langle [p_{\mathrm{T}}] \rangle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}}$ $\in$ $|\eta| \leq 0.8$.

$\gamma_{\langle [p_{\mathrm{T}}] \rangle}/\gamma_{\langle [p_{\mathrm{T}}] \rangle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $-3.7<\eta<-1.7$ and $2.8 < \eta <5.1$.

$\Gamma_{\langle [p_{\mathrm{T}}] \rangle}/\Gamma_{\langle [p_{\mathrm{T}}] \rangle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $|\eta| \leq 0.8$.

$\Gamma_{\langle [p_{\mathrm{T}}] \rangle}/\Gamma_{\langle [p_{\mathrm{T}}] angle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}}$ $\in$ $|\eta| \leq 0.8$.

$\Gamma_{\langle [p_{\mathrm{T}}] \rangle}/\Gamma_{\langle [p_{\mathrm{T}}] \rangle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $-3.7<\eta<-1.7$ and $2.8 < \eta <5.1$.

$\kappa_{\langle [p_{\mathrm{T}}] \rangle}/\kappa_{\langle [p_{\mathrm{T}}] \rangle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $|\eta| \leq 0.8$.

$\kappa_{\langle [p_{\mathrm{T}}] \rangle}/\kappa_{\langle [p_{\mathrm{T}}] \rangle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $E_{\mathrm{T}}$ $\in$ $|\eta| \leq 0.8$.

$\kappa_{\langle [p_{\mathrm{T}}] \rangle}/\kappa_{\langle [p_{\mathrm{T}}] \rangle}^{0-5\%}$ as a function of $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $-3.7<\eta<-1.7$ and $2.8 < \eta <5.1$.

Correlation between $\langle p_{\mathrm{T}} \rangle / \langle p_{\mathrm{T}} \rangle ^{\mathrm{0-5\%}}$ and $\langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle / \langle \mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta \rangle ^{\mathrm{0-5\%}}$ in $\mathrm{Pb}-\mathrm{Pb}$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.02~\mathrm{TeV}$. Data points are shown for centrality estimator based on $N_{\mathrm{ch}}$ $\in$ $0.5 \leq |\eta| \leq 0.8$ with $0.45 \leq p_{\mathrm{T}} \leq 50~\mathrm{GeV}/c.$.