Normalised differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n\text{ch}$ in $ n_\text{ch} \geq 80$.

The $\chi^2$ and NDF for measured absolute differential cross-sections obtained by comparing the different predictions with the unfolded data. Global($n_\text{ch},\Sigma_{n_{\text{ch}}} p_{\text{T}}$) denotes the scenario in which the covariance matrix is built including the correlations of systematic uncertainties between the two observables $n_{\text{ch}}$ and $\Sigma_{n_{\text{ch}}} p_{\text{T}}$

Absolute differential cross-section as a function of $n_\text{ch}$.

Absolute differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n\text{ch}$ in $ n_\text{ch} \geq 80$.

FIG. 2: a) left side: The correlation data for the ratio of the histograms of same-event-pairs to mixed-event-pairs for unlike-sign charged pairs, shown in a two-dimensional (2-D) perspective plot $\Delta\phi$ - $\Delta\eta$. The plot was normalized to a mean of 1. b) right side: The similar correlation data for like-sign charge pairs.

FIG. 3: a) left side: Folded correlation data for unlike-sign charge pairs on $\Delta\phi$ and $\Delta\eta$ based on demonstrated symmetries in the data. This increases the statistics in a typical bin by a factor of four. Henceforth we will be dealing with folded data only. b) right side: Similar folded data for like-sign charge pairs.

FIG. 3: a) left side: Folded correlation data for unlike-sign charge pairs on $\Delta\phi$ and $\Delta\eta$ based on demonstrated symmetries in the data. This increases the statistics in a typical bin by a factor of four. Henceforth we will be dealing with folded data only. b) right side: Similar folded data for like-sign charge pairs.

FIG. 4: “(Color online)”a) left side: The 2 dimensional (2-D) approximately gaussian signal shape equation (4) from the fit to the normalized and folded unlike-sign charge pairs signal data. b) right side: The unlike-sign charge pairs signal data corresponding to the adjoining signal function on the left side.

FIG. 4: “(Color online)”a) left side: The 2 dimensional (2-D) approximately gaussian signal shape equation (4) from the fit to the normalized and folded unlike-sign charge pairs signal data. b) right side: The unlike-sign charge pairs signal data corresponding to the adjoining signal function on the left side.

FIG. 5 :“(Color online)”a) left side: A perspective plot of the fit to the 2-D like-sign charge pairs signal shape of equation (5). b) right side: Like-sign charge pairs signal data corresponding to the adjacent signal fit on the left side.

FIG. 5: “(Color online)”a) left side: A perspective plot of the fit to the 2-D like-sign charge pairs signal shape of equation (5). b) right side: Like-sign charge pairs signal data corresponding to the adjacent signal fit on the left side.

FIG. 6: “(Color online)”a) left side: The charge dependent (CD) signal shape is a 2 dimensional (2-D) approximate gaussian which is symmetrical. The average gaussian width is 27.5$^{\circ}$ $\pm$ 3.2$^{\circ}$ (0.48 $\pm$ 0.056 rad) both in $\Delta\phi$ and $\Delta\eta$. Thus we are observing a greater probability for unlike-sign charge pairs of particles than for like-sign charge pairs emitted randomly on 2-D $\eta\phi$ directions. b) right side: The CD signal data corresponding to the adjacent signal fit on the left.

FIG. 6: “(Color online)”a) left side: The charge dependent (CD) signal shape is a 2 dimensional (2-D) approximate gaussian which is symmetrical. The average gaussian width is 27.5$^{\circ}$ $\pm$ 3.2$^{\circ}$ (0.48 $\pm$ 0.056 rad) both in $\Delta\phi$ and $\Delta\eta$. Thus we are observing a greater probability for unlike-sign charge pairs of particles than for like-sign charge pairs emitted randomly on 2-D $\eta\phi$ directions. b) right side: The CD signal data corresponding to the adjacent signal fit on the left.

FIG. 7: “(Color online)”a) left side: The fit to the charge independent (CI) signal shape which is the sum of the fits of like-sign plus unlike-sign charge pairs signals. The 2-D gaussian equivalent RMS signal has a $\Delta\phi$ width of about 32$^{\circ}$ (0.52 rad), and $\Delta\eta$ width of about 66$^{\circ}$ (1.15 rad) which is about double the $\Delta\phi$ width. b) right side: The CI signal data corresponding to the adjacent fit on the left.

FIG. 7: “(Color online)”a) left side: The fit to the charge independent (CI) signal shape which is the sum of the fits of like-sign plus unlike-sign charge pairs signals. The 2-D gaussian equivalent RMS signal has a $\Delta\phi$ width of about 32$^{\circ}$ (0.52 rad), and $\Delta\eta$ width of about 66$^{\circ}$ (1.15 rad) which is about double the $\Delta\phi$ width. b) right side: The CI signal data corresponding to the adjacent fit on the left.

FIG. 8: a) left side: The charge independent (CI) signal data with the lower range of elliptic flow amplitude and the best $\chi^2$ (the same as Figure 7b) plotted as a 2-D perspective plot on $\Delta\phi$ vs. $\Delta\eta$. b) right side: The charge independent (CI) signal data with our maximum elliptic flow amplitude used in our analysis ($\chi^2$ was worse by 1$\sigma$).

FIG. 8: a) left side: The charge independent (CI) signal data with the lower range of elliptic flow amplitude and the best $\chi^2$ (the same as Figure 7b) plotted as a 2-D perspective plot on $\Delta\phi$ vs. $\Delta\eta$. b) right side: The charge independent (CI) signal data with our maximum elliptic flow amplitude used in our analysis ($\chi^2$ was worse by 1$\sigma$).

Differential ep cross section for dijet production as a function of the average pseudorapidity the two jets in two transverse energy regions and in the Y region 0.1 to 0.5.

Differential ep cross section for dijet production as a function of the average pseudorapidity the two jets in two transverse energy regions and in the Y region 0.5 to 0.9.

Differential ep cross section for dijet production as a function of the photon momentum fraction X(C=GAMMA) in two regions of proton fractional momentum XP.

Differential ep cross section for dijet production as a function of the proton momentum fraction XP in two regions of photon fractional momentum X(C=GAMMA).

Differential ep cross section for dijet production as a function of the photon momentum fraction X(C=GAMMA) in two regions maximum jet transverseenergy.

Differential ep cross section for dijet production as a function of COS(THETA*) in two regions of fractional photon momentum fraction X(C=GAMMA).

Differential ep cross section for dijet production as a function of COS(THETA*) for dijet masses > 65 GeV in two regions of fractional photon momentum fraction X(C=GAMMA).

Data from Fig 3b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.

Data from Fig 3c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. The uncertainties are applied symmetrically, though the cross section cannot go below zero in the first bin.

Data from Fig 3c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. The uncertainties are applied symmetrically, though the cross section cannot go below zero in the first bin.

Data from FigAux 4 and FigAux 8a-16a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for beta = 0, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 4 and Fig 8a-16a. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for beta = 0, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 4 and FigAux 8b-16b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 4 and Fig 8b-16b. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 8c-16c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 8c-16c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$(lead) > 600 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data; the uncertainties from the calculations are shown on each one. The distributions are normalized to the integrated cross section, sigma(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6a. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 0. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6a. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 0. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6b. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 1. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6b. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 1. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 6c. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 2. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from Fig 6c. The summed covariance matrices of the systematic and statistical uncertainties for the combined $p_T$ and $log_{10}(\rho^2)$ bins for $\beta$ = 2. Each group of 10 bins corresponds to a bin of $p_T$ in {600, 650, 700, 750, 800, 850, 900, 950, 1000, ∞ }; each bin within the $p_T$ bin corresponds to 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.

Data from FigAux 7a. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 0, inclusive in $p_T$.

Data from Fig 7a. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 0, inclusive in $p_T$.

Data from FigAux 7b. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 1, inclusive in $p_T$.

Data from Fig 7b. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 1, inclusive in $p_T$.

Data from FigAux 7c. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 2, inclusive in $p_T$.

Data from Fig 7c. The summed covariance matrices of the systematic and statistical uncertainties for the $log_{10}(\rho^2)$ bins for $\beta$ = 2, inclusive in $p_T$.

Cross sections ratio J/PSI/Drell-Yan as a function of CENTRALITY(NAME=b) obtained withthe ET Centrality estimator.

Cross sections ratio J/PSI/Drell-Yan as a function of CENTRALITY(NAME=L) obtained withthe ET Centrality estimator.

Centrality classes based on the forward energy measurement errors are rms values obtained with fit.

Cross sections ratio J/PSI/Drell-Yan as a function of E(NAME=ZDC) obtained withthe E(NAME=ZDC) Centrality estimator.

Cross sections ratio J/PSI/Drell-Yan as a function of N(NAME=PART) obtained withthe E(NAME=ZDC) Centrality estimator.

Cross sections ratio J/PSI/Drell-Yan as a function of CENTRALITY(NAME=b) obtained withthe E(NAME=ZDC) Centrality estimator.

Cross sections ratio J/PSI/Drell-Yan as a function of CENTRALITY(NAME=L) obtained withthe E(NAME=ZDC) Centrality estimator.

Centrality classes based on the Charged particle multiplicity : DN/DYRAP at YRAP=0 errors are rms values obtained with fit.

Cross sections ratio J/PSI/Drell-Yan as a function of N(NAME=CH) obtained withthe N(NAME=CH) Centrality estimator.

Cross sections ratio J/PSI/Drell-Yan as a function of N(NAME=PART) obtained withthe N(NAME=CH) Centrality estimator.

Cross sections ratio J/PSI/Drell-Yan as a function of CENTRALITY(NAME=b) obtained withthe N(NAME=CH) Centrality estimator.

Cross sections ratio J/PSI/Drell-Yan as a function of CENTRALITY(NAME=L) obtained withthe N(NAME=CH) Centrality estimator.