$a_2(1320)$ differential cross section in the m=-2 projection split into different reflectivity components, $\frac{d\sigma^+_{-2}}{dt}$ and $\frac{d\sigma^-_{-2}}{dt}$, in bins of four-momentum transfer. The first uncertainty is statistical, the second systematic.

$a_2(1320)$ differential cross section in the m=0 projection split into different reflectivity components, $\frac{d\sigma^+_{0}}{dt}$ and $\frac{d\sigma^-_{0}}{dt}$, in bins of four-momentum transfer. The first uncertainty is statistical, the second systematic.

$a_2(1320)$ differential cross section in the m=+1 projection split into different reflectivity components, $\frac{d\sigma^+_{+1}}{dt}$ and $\frac{d\sigma^-_{+1}}{dt}$, in bins of four-momentum transfer. The first uncertainty is statistical, the second systematic.

$a_2(1320)$ differential cross section in the m=+2 projection split into different reflectivity components, $\frac{d\sigma^+_{+2}}{dt}$ and $\frac{d\sigma^-_{+2}}{dt}$, in bins of four-momentum transfer. The first uncertainty is statistical, the second systematic.

Measured values of $R(\tau/e)$ in the different $p_T$ bins. The first row gives the main inclusive measurement of $R(\tau/e)$.

Breakdown of systematic uncertainties in the fit to data. Different individual components used in the fit are combined into categories. The size of the impact each group of uncertainties has on $R(\tau/e)$, which is shown in the second column, is assessed by subtracting in quadrature the uncertainty from the nominal fit and the fit with the relevant nuisance parameters fixed to their post-fit values. The group 'Other sources' includes the uncertainties due to trigger, JVT, luminosity, and pile-up.

Distributions of $|d_0|$ for the $\mu e$ channel of the nominal sample in $7 < p_T < 10$ GeV bin. The data are represented by black markers. The different contributions to the nominal sample, taken from simulation after the fit (`Post-Fit'), are shown by stacked histograms. The bottom panels show the ratio of the data to the simulation prediction after the fit. The systematic uncertainties in the prediction with the constraints from the fit and the correlation between the nuisance parameters applied are represented by the blue hatched bands.

Distributions of $|d_0|$ for the $\mu e$ channel of the nominal sample in $10 < p_T < 20$ GeV bin. The data are represented by black markers. The different contributions to the nominal sample, taken from simulation after the fit (`Post-Fit'), are shown by stacked histograms. The bottom panels show the ratio of the data to the simulation prediction after the fit. The systematic uncertainties in the prediction with the constraints from the fit and the correlation between the nuisance parameters applied are represented by the blue hatched bands.

Distributions of $|d_0|$ for the $\mu e$ channel of the nominal sample in $20 < p_T < 250$ GeV bin. The data are represented by black markers. The different contributions to the nominal sample, taken from simulation after the fit (`Post-Fit'), are shown by stacked histograms. The bottom panels show the ratio of the data to the simulation prediction after the fit. The systematic uncertainties in the prediction with the constraints from the fit and the correlation between the nuisance parameters applied are represented by the blue hatched bands.

Distributions of $|d_0|$ for the $e e$ channel of the nominal sample in $5 < p_T < 10$ GeV bin. The data are represented by black markers. The different contributions to the nominal sample, taken from simulation after the fit (`Post-Fit'), are shown by stacked histograms. The bottom panels show the ratio of the data to the simulation prediction after the fit. The systematic uncertainties in the prediction with the constraints from the fit and the correlation between the nuisance parameters applied are represented by the blue hatched bands.

Distributions of $|d_0|$ for the $e e$ channel of the nominal sample in $10 < p_T < 20$ GeV bin. The data are represented by black markers. The different contributions to the nominal sample, taken from simulation after the fit (`Post-Fit'), are shown by stacked histograms. The bottom panels show the ratio of the data to the simulation prediction after the fit. The systematic uncertainties in the prediction with the constraints from the fit and the correlation between the nuisance parameters applied are represented by the blue hatched bands.

Distributions of $|d_0|$ for the $e e$ channel of the nominal sample in $20 < p_T < 250$ GeV bin. The data are represented by black markers. The different contributions to the nominal sample, taken from simulation after the fit (`Post-Fit'), are shown by stacked histograms. The bottom panels show the ratio of the data to the simulation prediction after the fit. The systematic uncertainties in the prediction with the constraints from the fit and the correlation between the nuisance parameters applied are represented by the blue hatched bands.

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

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

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

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

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

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

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

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

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

Observed / Expected 95% CL limit ratio in SR1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

90% CL WIMP SDn cross sections, including sensitivities and nuclear structure uncertainties

90% CL WIMP SDp cross sections, including sensitivities and nuclear structure uncertainties

90% CL WIMP SDp cross sections, including sensitivities and nuclear structure uncertainties

Data selection efficiency as a function of nuclear recoil energy

Data selection efficiency as a function of nuclear recoil energy

Data points used in analysis in log_10(S2)-S1 space

Data points used in analysis in log_10(S2)-S1 space

Self-Normalized $J/\psi$ yields in the same arms before and after dimuon subtraction and the comparison with PYTHIA 8 simulations

Self-normalized $\psi(2S)$ to $J/\psi$ yields ratio as a function of Self-normalized $N_{ch}$ in the same arms

Self-normalized $\psi(2S)$ to $J/\psi$ yields ratio as a function of Self-normalized $N_{ch}$ in opposite arms

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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