The observed 95% CL exclusion limits as a function of κ_2V (obtained using the signal strength μ_VBF as the POI) from the combined ggF and VBF signal regions, as shown by the solid black line. The value of κ_λ is fixed to 1. The blue and yellow bands show respectively the 1σ and 2σ bands around the expected exclusion limits, which are shown by the dashed black line. The expected exclusion limits are obtained using a fit to the data with the background-only hypothesis. The dark red line shows in the predicted VBF HH cross-section as a function of κ_2V.

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

The expected and observed profile likelihood ratio scans for κ_λ coupling modifier, shown by the solid black line, using the coupling modifier as the POIs. The value of κ_2V is fixed to 1. The dashed blue line shows the expected profile likelihood ratio, as obtained using a fit to the data with the background-only hypothesis. The pink line indicates the 2σ exclusion boundary.

The expected and observed profile likelihood ratio scans for the κ_2V coupling modifier, shown by the solid black line, using the coupling modifier as the POIs. The value of κ_λ is fixed to 1. The dashed blue line shows the expected profile likelihood ratio, as obtained using a fit to the data with the background-only hypothesis. The pink line indicates the 2σ exclusion boundary.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The nuclear suppression factor $S^{\mathrm{J}/\psi}$ of incoherent $\mathrm{J}/\psi$ meson photoproduction as a function of Bjorken $x$ in PbPb ultra-peripheral collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV. The $x$ values are evaluated at the centers of their corresponding rapidity ranges. The theoretical uncertainties are due to the uncertainties in the photon flux.

The ratio between incoherent and coherent $\mathrm{J}/\psi$ meson photoproduction cross section as a function of photon-nuclear center-of-mass energy per nucleon $W_{\gamma \mathrm{N}}^{\mathrm{Pb}}$, measured in PbPb ultra-peripheral collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV. The $W_{\gamma \mathrm{N}}^{\mathrm{Pb}}$ values used correspond to the center of each rapidity range. The theoretical uncertainties is due to the uncertainties in the photon flux.

The total covariance matrix of the total incoherent photoproduction cross section as a function of photon-nuclear center-of-mass energy per nucleon $W_{\gamma \mathrm{N}}^{\mathrm{Pb}}$. The covariance matrix includes both the experimental and theoretical (photon flux) uncertainties. The bins are ordered as increasing in $W_{\gamma \mathrm{N}}^{\mathrm{Pb}}$.

The experimental covariance matrix of the total incoherent photoproduction cross section as a function of photon-nuclear center-of-mass energy per nucleon $W_{\gamma \mathrm{N}}^{\mathrm{Pb}}$. The bins are ordered as increasing in $W_{\gamma \mathrm{N}}^{\mathrm{Pb}}$.

The theoretical (photon flux) covariance matrix of the total incoherent photoproduction cross section as a function of photon-nuclear center-of-mass energy per nucleon $W_{\gamma \mathrm{N}}^{\mathrm{Pb}}$. The bins are ordered as increasing in $W_{\gamma \mathrm{N}}^{\mathrm{Pb}}$.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, trig} < 4.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, trig} < 4.0$ GeV/$c$ and $2.0 < p_\mathrm{T, assoc} < 3.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 4.0$ GeV/$c$. The multiplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $2.0 < p_\mathrm{T, assoc} < 3.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $3.0 < p_\mathrm{T, assoc} < 4.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 8.0$ GeV/$c$. The multiplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $1.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 2.0$ GeV/$c$. The multiplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $2.0 < p_\mathrm{T, trig} < 3.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $2.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 3.0$ GeV/$c$. The multiplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, trig} < 4.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, trig} < 4.0$ GeV/$c$ and $2.0 < p_\mathrm{T, assoc} < 3.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 4.0$ GeV/$c$. The multiplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $2.0 < p_\mathrm{T, assoc} < 3.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $3.0 < p_\mathrm{T, assoc} < 4.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the near-side width $\sigma$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 8.0$ GeV/$c$. The multiplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $1.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 2.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $2.0 < p_\mathrm{T, trig} < 3.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $2.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 3.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, trig} < 4.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, trig} < 4.0$ GeV/$c$ and $2.0 < p_\mathrm{T, assoc} < 3.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 4.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $2.0 < p_\mathrm{T, assoc} < 3.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $3.0 < p_\mathrm{T, assoc} < 4.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 8.0$ GeV/$c$. The mulitplicity is estimated with midrapidity multiplicity estimator ($|\eta|<1.0,\,p_\mathrm{T}>0.2$ GeV/$c$).

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $1.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 2.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $2.0 < p_\mathrm{T, trig} < 3.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $2.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 3.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, trig} < 4.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, trig} < 4.0$ GeV/$c$ and $2.0 < p_\mathrm{T, assoc} < 3.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $3.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 4.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $1.0 < p_\mathrm{T, assoc} < 2.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $2.0 < p_\mathrm{T, assoc} < 3.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, trig} < 8.0$ GeV/$c$ and $3.0 < p_\mathrm{T, assoc} < 4.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Multiplicity dependence of the fragmentation yield $Y^{frag}$ in pp collisions at $\sqrt{s_{\rm NN}} = 13$ TeV. Obtained in transverse momentum intervals $4.0 < p_\mathrm{T, assoc} < p_\mathrm{T, trig} < 8.0$ GeV/$c$. The mulitplicity is estimated with forward multiplicity estimator.

Normalized $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ in $6.5<p_T<30.0\,GeV$ and $ 1 < y_{CM} < 1.935$ as functions of normalized $\text{N}^{{\text{corr.}}}_{\text{track}}$

Normalized $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ in $6.5<p_T<30.0\,GeV$ and $ -2.865 < y_{CM} < 1.935$ as functions of normalized $\text{N}^{{\text{corr.}}}_{\text{track}}$

Normalized $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ in $3.0<p_T<6.5\,GeV$ and $ -2.865 < y_{CM} < -2 $ as functions of normalized $\text{N}^{{\text{corr.}}}_{\text{track}}$

Normalized $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ in $3.0<p_T<6.5\,GeV$ and $ 1 < y_{CM} < 1.935 $ as functions of normalized $\text{N}^{{\text{corr.}}}_{\text{track}}$

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

The figure shows the measurement of the top quark pair production cross-section, $\sigma_{t\bar{t}}$, in lead-lead (Pb+Pb) collisions at a center-of-mass energy of $\sqrt{s_{\mathrm{NN}}} = 5.02$ TeV, based on 1.9 nb$^{-1}$ of data.

Isolated-photon cross section ratio of pp collisions at $\sqrt{s}=13~\mathrm{TeV}$ over $\sqrt{s}=5.02~\mathrm{TeV}$ in data (left column) and NLO pQCD calculation from JETPHOX (right column) for $R=0.4$. Data statistical and systematic uncertainties are provided. The theory scale and PDF uncertainties are provided. The data normalisation uncertainty is provided in the paper.

Isolated-photon cross section ratio of pp collisions at $\sqrt{s}=7~\mathrm{TeV}$ over $\sqrt{s}=5.02~\mathrm{TeV}$ in data (left column) and NLO pQCD calculation from JETPHOX (right column) for $R=0.4$. Data statistical and systematic uncertainties are provided. The theory scale and PDF uncertainties are provided. The data normalisation uncertainty is provided in the paper.

Nuclear modification factor ($R_{\rm AA}$) of isolated photons for Pb$-$Pb and pp collisions at $\sqrt{s_{\mathrm{NN}}}=5.02~\mathrm{TeV}$. $R_{\rm AA}$ obtained with a cone size value of $R=0.2$. Each column for each Pb$-$Pb collisions centrality class. The last column for the NLO pQCD JETPHOX calculations, Pb$-$Pb (nPDF) over pp (PDF). Data statistical and systematic uncertainties are provided. The theory scale and PDF uncertainties are provided.

Nuclear modification factor ($R_{\rm AA}$) of isolated photons for Pb$-$Pb and pp collisions at $\sqrt{s_{\mathrm{NN}}}=5.02~\mathrm{TeV}$. $R_{\rm AA}$ obtained with a cone size value of $R=0.4$. Each column for each Pb$-$Pb collisions centrality class. The last column for the NLO pQCD JETPHOX calculations, Pb$-$Pb (nPDF) over pp (PDF). Data statistical and systematic uncertainties are provided. The theory scale and PDF uncertainties are provided.

Ratios $R_{\rm CSP}$ and $R_{\rm CSC}$ of isolated photons cross sections in data for Pb$-$Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02~\mathrm{TeV}$. $R_{\rm CSP}$, ratio central over semi-peripheral collisions 50$-$70%. $R_{\rm CSC}$, ratio central over semi-central collisions 30$-$50%. Ratios obtained with a cone size value of $R=0.2$. Data statistical and systematic uncertainties are provided.

Ratios $R_{\rm CSP}$ and $R_{\rm CSC}$ of isolated photons cross sections in data for Pb$-$Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02~\mathrm{TeV}$. $R_{\rm CSP}$, ratio central over semi-peripheral collisions 50$-$70%. $R_{\rm CSC}$, ratio central over semi-central collisions 30$-$50%. Ratios obtained with a cone size value of $R=0.4$. Data statistical and systematic uncertainties are provided.

Self-normalised ZN energy as a function of the self-normalised charged-particle-multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV in the high-ZN-energy classification (V0M+SPDClusters event classes).

Self-normalised ZN energy as a function of the self-normalised charged-particle-multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV in the low-ZN-energy classification (V0M+SPDClusters event classes).

$K^{0}_{S}$ transverse momentum spectrum in pp collisions at $\sqrt{s}$ = 13 TeV in the high-ZN-energy classification (V0M+SPDClusters event classes).

$\Lambda+\overline{\Lambda}$ transverse momentum spectrum in pp collisions at $\sqrt{s}$ = 13 TeV in the high-ZN-energy classification (V0M+SPDClusters event classes).

$\Xi^{-}+\overline{\Xi}^{+}$ transverse momentum spectrum in pp collisions at $\sqrt{s}$ = 13 TeV in the high-ZN-energy classification (V0M+SPDClusters event classes).

$K^{0}_{S}$ transverse momentum yield ratios to class III in the high-ZN-energy classification (V0M+SPDClusters event classes) in pp collisions at $\sqrt{s}$ = 13 TeV.

$\Lambda+\overline{\Lambda}$ transverse momentum yield ratios to class III in the high-ZN-energy classification (V0M+SPDClusters event classes) in pp collisions at $\sqrt{s}$ = 13 TeV.

$\Xi^{-}+\overline{\Xi}^{+}$ transverse momentum yield ratios to class III in the high-ZN-energy classification (V0M+SPDClusters event classes) in pp collisions at $\sqrt{s}$ = 13 TeV.

$K^{0}_{S}$ transverse momentum spectrum in pp collisions at $\sqrt{s}$ = 13 TeV in the high-local-multiplicity classification (V0M+SPDClusters event classes).

$\Lambda+\overline{\Lambda}$ transverse momentum spectrum in pp collisions at $\sqrt{s}$ = 13 TeV in the high-local-multiplicity classification (V0M+SPDClusters event classes).

$\Xi^{-}+\overline{\Xi}^{+}$ transverse momentum spectrum in pp collisions at $\sqrt{s}$ = 13 TeV in the high-local-multiplicity classification (V0M+SPDClusters event classes).

$K^{0}_{S}$ transverse momentum yield ratios to class IV in the high-local-multiplicity classification (V0M+SPDClusters event classes) in pp collisions at $\sqrt{s}$ = 13 TeV.

$\Lambda+\overline{\Lambda}$ transverse momentum yield ratios to class IV in the high-local-multiplicity classification (V0M+SPDClusters event classes) in pp collisions at $\sqrt{s}$ = 13 TeV.

$\Xi^{-}+\overline{\Xi}^{+}$ transverse momentum yield ratios to class IV in the high-local-multiplicity classification (V0M+SPDClusters event classes) in pp collisions at $\sqrt{s}$ = 13 TeV.

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised charged-particle multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV in the standalone classification (V0M event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised $ZN$ signal in pp collisions at $\sqrt{s}$ = 13 TeV in the standalone classification (V0M event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised charged-particle multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV in the high-local-multiplicity classification (V0M+SPDClusters event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised charged-particle multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV in the low-local-multiplicity classification (V0M+SPDClusters event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised charged-particle multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV in the high-ZN-energy classification (V0M+SPDClusters event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised charged-particle multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV in the low-ZN-energy classification (V0M+SPDClusters event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised $ZN$ signal in pp collisions at $\sqrt{s}$ = 13 TeV in the high-local-multiplicity classification (V0M+SPDClusters event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised $ZN$ signal in pp collisions at $\sqrt{s}$ = 13 TeV in the low-local-multiplicity classification (V0M+SPDClusters event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised $ZN$ signal in pp collisions at $\sqrt{s}$ = 13 TeV in the high-ZN-energy classification (V0M+SPDClusters event classes).

Self-normalised yield ratios of $K^{0}_{S}$, $\Lambda+\overline{\Lambda}$, and $\Xi^{-}+\overline{\Xi}^{+}$ as a function of the self-normalised $ZN$ signal in pp collisions at $\sqrt{s}$ = 13 TeV in the low-ZN-energy classification (V0M+SPDClusters event classes).