Intensive skewness of $\langle p_\mathrm{T}\rangle$ as a function of $\langle\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\rangle^{1/3}_{|\eta|<0.5}$ in pp collisions at $\sqrt{s}$ = 5.02 TeV.

Intensive skewness of $\langle p_\mathrm{T}\rangle$ as a function of $\langle\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\rangle^{1/3}_{|\eta|<0.5}$ in Xe$-$Xe collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV.

Intensive skewness of $\langle p_\mathrm{T}\rangle$ as a function of $\langle\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\rangle^{1/3}_{|\eta|<0.5}$ in Pb$-$Pb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV.

Kurtosis of $\langle p_\mathrm{T}\rangle$ as a function of $\langle\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\rangle^{1/3}_{|\eta|<0.5}$ in pp collisions at $\sqrt{s}$ = 5.02 TeV.

Kurtosis of $\langle p_\mathrm{T}\rangle$ as a function of $\langle\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\rangle^{1/3}_{|\eta|<0.5}$ in Xe$-$Xe collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV.

Kurtosis of $\langle p_\mathrm{T}\rangle$ as a function of $\langle\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\rangle^{1/3}_{|\eta|<0.5}$ in Pb$-$Pb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV.

Summary of results for bottom muon v2 as a function of pT for different centrality. Uncertainties are statistical and systematic, respectively.

Summary of results for charm muon v3 as a function of pT for different centrality. Uncertainties are statistical and systematic, respectively.

Summary of results for bottom muon v3 as a function of pT for different centrality. Uncertainties are statistical and systematic, respectively.

The second flow harmonic measured with the two-particle cumulants as a function of transverse momentum in the event activity bin of >80 GeV.

The second flow harmonic measured with the four-particle cumulants as a function of transverse momentum in the event activity bin of 25-40 GeV.

The second flow harmonic measured with the four-particle cumulants as a function of transverse momentum in the event activity bin of 40-55 GeV.

The second flow harmonic measured with the four-particle cumulants as a function of transverse momentum in the event activity bin of 55-80 GeV.

The second flow harmonic measured with the four-particle cumulants as a function of transverse momentum in the event activity bin of >80 GeV.

The second-order harmonic, v2, integrated over pT and eta, calculated with two-particle cumulants as a function of Sum ET^Pb.

The second-order harmonic, v2, integrated over pT and eta, calculated with four-particle cumulants as a function of Sum ET^Pb.

The corrected per-event transverse momentum distribution of Z bosons in the centrality region 10-20%.

The corrected per-event transverse momentum distribution of Z bosons in the centrality region 20-40%.

The corrected per-event transverse momentum distribution of Z bosons in the centrality region 40-80%.

Combined results for the centrality (Npart) dependence of Z boson yields divided by Ncoll for the PT range > 0 GeV/c. The systematic error includes the uncertainty in Ncoll.

Combined results for the centrality (Npart) dependence of Z boson yields divided by Ncoll for the PT range 0 to 10 GeV/c. The systematic error includes the uncertainty in Ncoll.

Combined results for the centrality (Npart) dependence of Z boson yields divided by Ncoll for the PT range 10 to 30 GeV/c. The systematic error includes the uncertainty in Ncoll.

Combined results for the centrality (Npart) dependence of Z boson yields divided by Ncoll for the PT range > 30 GeV/c. The systematic error includes the uncertainty in Ncoll.

$v_{5}$ data for various $q_2$ bins, Centrality 0-5%.

$v_{2}$ data for various $q_2$ bins, Centrality 5-10%.

$v_{3}$ data for various $q_2$ bins, Centrality 5-10%.

$v_{4}$ data for various $q_2$ bins, Centrality 5-10%.

$v_{5}$ data for various $q_2$ bins, Centrality 5-10%.

$v_{2}$ data for various $q_2$ bins, Centrality 10-15%.

$v_{3}$ data for various $q_2$ bins, Centrality 10-15%.

$v_{4}$ data for various $q_2$ bins, Centrality 10-15%.

$v_{5}$ data for various $q_2$ bins, Centrality 10-15%.

$v_{2}$ data for various $q_2$ bins, Centrality 15-20%.

$v_{3}$ data for various $q_2$ bins, Centrality 15-20%.

$v_{4}$ data for various $q_2$ bins, Centrality 15-20%.

$v_{5}$ data for various $q_2$ bins, Centrality 15-20%.

$v_{2}$ data for various $q_2$ bins, Centrality 20-25%.

$v_{3}$ data for various $q_2$ bins, Centrality 20-25%.

$v_{4}$ data for various $q_2$ bins, Centrality 20-25%.

$v_{5}$ data for various $q_2$ bins, Centrality 20-25%.

$v_{2}$ data for various $q_2$ bins, Centrality 25-30%.

$v_{3}$ data for various $q_2$ bins, Centrality 25-30%.

$v_{4}$ data for various $q_2$ bins, Centrality 25-30%.

$v_{5}$ data for various $q_2$ bins, Centrality 25-30%.

$v_{2}$ data for various $q_2$ bins, Centrality 30-35%.

$v_{3}$ data for various $q_2$ bins, Centrality 30-35%.

$v_{4}$ data for various $q_2$ bins, Centrality 30-35%.

$v_{5}$ data for various $q_2$ bins, Centrality 30-35%.

$v_{2}$ data for various $q_2$ bins, Centrality 35-40%.

$v_{3}$ data for various $q_2$ bins, Centrality 35-40%.

$v_{4}$ data for various $q_2$ bins, Centrality 35-40%.

$v_{5}$ data for various $q_2$ bins, Centrality 35-40%.

$v_{2}$ data for various $q_2$ bins, Centrality 40-45%.

$v_{3}$ data for various $q_2$ bins, Centrality 40-45%.

$v_{4}$ data for various $q_2$ bins, Centrality 40-45%.

$v_{5}$ data for various $q_2$ bins, Centrality 40-45%.

$v_{2}$ data for various $q_2$ bins, Centrality 45-50%.

$v_{3}$ data for various $q_2$ bins, Centrality 45-50%.

$v_{4}$ data for various $q_2$ bins, Centrality 45-50%.

$v_{5}$ data for various $q_2$ bins, Centrality 45-50%.

$v_{2}$ data for various $q_2$ bins, Centrality 50-55%.

$v_{3}$ data for various $q_2$ bins, Centrality 50-55%.

$v_{4}$ data for various $q_2$ bins, Centrality 50-55%.

$v_{5}$ data for various $q_2$ bins, Centrality 50-55%.

$v_{2}$ data for various $q_2$ bins, Centrality 55-60%.

$v_{3}$ data for various $q_2$ bins, Centrality 55-60%.

$v_{4}$ data for various $q_2$ bins, Centrality 55-60%.

$v_{5}$ data for various $q_2$ bins, Centrality 55-60%.

$v_{2}$ data for various $q_2$ bins, Centrality 60-65%.

$v_{3}$ data for various $q_2$ bins, Centrality 60-65%.

$v_{4}$ data for various $q_2$ bins, Centrality 60-65%.

$v_{5}$ data for various $q_2$ bins, Centrality 60-65%.

$v_{2}$ data for various $q_2$ bins, Centrality 65-70%.

$v_{3}$ data for various $q_2$ bins, Centrality 65-70%.

$v_{4}$ data for various $q_2$ bins, Centrality 65-70%.

$v_{5}$ data for various $q_2$ bins, Centrality 65-70%.

$v_{2}$ data for various $q_2$ bins, Centrality 0-10%.

$v_{3}$ data for various $q_2$ bins, Centrality 0-10%.

$v_{4}$ data for various $q_2$ bins, Centrality 0-10%.

$v_{5}$ data for various $q_2$ bins, Centrality 0-10%.

$v_{2}$ data for various $q_2$ bins, Centrality 10-20%.

$v_{3}$ data for various $q_2$ bins, Centrality 10-20%.

$v_{4}$ data for various $q_2$ bins, Centrality 10-20%.

$v_{5}$ data for various $q_2$ bins, Centrality 10-20%.

$v_{2}$ data for various $q_2$ bins, Centrality 20-30%.

$v_{3}$ data for various $q_2$ bins, Centrality 20-30%.

$v_{4}$ data for various $q_2$ bins, Centrality 20-30%.

$v_{5}$ data for various $q_2$ bins, Centrality 20-30%.

$v_{2}$ data for various $q_2$ bins, Centrality 30-40%.

$v_{3}$ data for various $q_2$ bins, Centrality 30-40%.

$v_{4}$ data for various $q_2$ bins, Centrality 30-40%.

$v_{5}$ data for various $q_2$ bins, Centrality 30-40%.

$v_{2}$ data for various $q_2$ bins, Centrality 40-50%.

$v_{3}$ data for various $q_2$ bins, Centrality 40-50%.

$v_{4}$ data for various $q_2$ bins, Centrality 40-50%.

$v_{5}$ data for various $q_2$ bins, Centrality 40-50%.

$v_{2}$ data for various $q_3$ bins, Centrality 0-5%.

$v_{3}$ data for various $q_3$ bins, Centrality 0-5%.

$v_{4}$ data for various $q_3$ bins, Centrality 0-5%.

$v_{5}$ data for various $q_3$ bins, Centrality 0-5%.

$v_{2}$ data for various $q_3$ bins, Centrality 5-10%.

$v_{3}$ data for various $q_3$ bins, Centrality 5-10%.

$v_{4}$ data for various $q_3$ bins, Centrality 5-10%.

$v_{5}$ data for various $q_3$ bins, Centrality 5-10%.

$v_{2}$ data for various $q_3$ bins, Centrality 10-15%.

$v_{3}$ data for various $q_3$ bins, Centrality 10-15%.

$v_{4}$ data for various $q_3$ bins, Centrality 10-15%.

$v_{5}$ data for various $q_3$ bins, Centrality 10-15%.

$v_{2}$ data for various $q_3$ bins, Centrality 15-20%.

$v_{3}$ data for various $q_3$ bins, Centrality 15-20%.

$v_{4}$ data for various $q_3$ bins, Centrality 15-20%.

$v_{5}$ data for various $q_3$ bins, Centrality 15-20%.

$v_{2}$ data for various $q_3$ bins, Centrality 20-25%.

$v_{3}$ data for various $q_3$ bins, Centrality 20-25%.

$v_{4}$ data for various $q_3$ bins, Centrality 20-25%.

$v_{5}$ data for various $q_3$ bins, Centrality 20-25%.

$v_{2}$ data for various $q_3$ bins, Centrality 25-30%.

$v_{3}$ data for various $q_3$ bins, Centrality 25-30%.

$v_{4}$ data for various $q_3$ bins, Centrality 25-30%.

$v_{5}$ data for various $q_3$ bins, Centrality 25-30%.

$v_{2}$ data for various $q_3$ bins, Centrality 30-35%.

$v_{3}$ data for various $q_3$ bins, Centrality 30-35%.

$v_{4}$ data for various $q_3$ bins, Centrality 30-35%.

$v_{5}$ data for various $q_3$ bins, Centrality 30-35%.

$v_{2}$ data for various $q_3$ bins, Centrality 35-40%.

$v_{3}$ data for various $q_3$ bins, Centrality 35-40%.

$v_{4}$ data for various $q_3$ bins, Centrality 35-40%.

$v_{5}$ data for various $q_3$ bins, Centrality 35-40%.

$v_{2}$ data for various $q_3$ bins, Centrality 40-45%.

$v_{3}$ data for various $q_3$ bins, Centrality 40-45%.

$v_{4}$ data for various $q_3$ bins, Centrality 40-45%.

$v_{5}$ data for various $q_3$ bins, Centrality 40-45%.

$v_{2}$ data for various $q_3$ bins, Centrality 45-50%.

$v_{3}$ data for various $q_3$ bins, Centrality 45-50%.

$v_{4}$ data for various $q_3$ bins, Centrality 45-50%.

$v_{5}$ data for various $q_3$ bins, Centrality 45-50%.

$v_{2}$ data for various $q_3$ bins, Centrality 50-55%.

$v_{3}$ data for various $q_3$ bins, Centrality 50-55%.

$v_{4}$ data for various $q_3$ bins, Centrality 50-55%.

$v_{5}$ data for various $q_3$ bins, Centrality 50-55%.

$v_{2}$ data for various $q_3$ bins, Centrality 55-60%.

$v_{3}$ data for various $q_3$ bins, Centrality 55-60%.

$v_{4}$ data for various $q_3$ bins, Centrality 55-60%.

$v_{5}$ data for various $q_3$ bins, Centrality 55-60%.

$v_{2}$ data for various $q_3$ bins, Centrality 60-65%.

$v_{3}$ data for various $q_3$ bins, Centrality 60-65%.

$v_{4}$ data for various $q_3$ bins, Centrality 60-65%.

$v_{5}$ data for various $q_3$ bins, Centrality 60-65%.

$v_{2}$ data for various $q_3$ bins, Centrality 65-70%.

$v_{3}$ data for various $q_3$ bins, Centrality 65-70%.

$v_{4}$ data for various $q_3$ bins, Centrality 65-70%.

$v_{5}$ data for various $q_3$ bins, Centrality 65-70%.

$v_{2}$ data for various $q_3$ bins, Centrality 0-10%.

$v_{3}$ data for various $q_3$ bins, Centrality 0-10%.

$v_{4}$ data for various $q_3$ bins, Centrality 0-10%.

$v_{5}$ data for various $q_3$ bins, Centrality 0-10%.

$v_{2}$ data for various $q_3$ bins, Centrality 10-20%.

$v_{3}$ data for various $q_3$ bins, Centrality 10-20%.

$v_{4}$ data for various $q_3$ bins, Centrality 10-20%.

$v_{5}$ data for various $q_3$ bins, Centrality 10-20%.

$v_{2}$ data for various $q_3$ bins, Centrality 20-30%.

$v_{3}$ data for various $q_3$ bins, Centrality 20-30%.

$v_{4}$ data for various $q_3$ bins, Centrality 20-30%.

$v_{5}$ data for various $q_3$ bins, Centrality 20-30%.

$v_{2}$ data for various $q_3$ bins, Centrality 30-40%.

$v_{3}$ data for various $q_3$ bins, Centrality 30-40%.

$v_{4}$ data for various $q_3$ bins, Centrality 30-40%.

$v_{5}$ data for various $q_3$ bins, Centrality 30-40%.

$v_{2}$ data for various $q_3$ bins, Centrality 40-50%.

$v_{3}$ data for various $q_3$ bins, Centrality 40-50%.

$v_{4}$ data for various $q_3$ bins, Centrality 40-50%.

$v_{5}$ data for various $q_3$ bins, Centrality 40-50%.

$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{2}$ correlation within each centrality.

$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{2}$ correlation within each centrality.

$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{2}$ correlation within each centrality.

$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{2}$ correlation within each centrality.

$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{2}$ correlation within each centrality.

$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{3}$ correlation within each centrality.

$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{3}$ correlation within each centrality.

$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{3}$ correlation within each centrality.

$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{3}$ correlation within each centrality.

$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.

$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.

$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.

$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.

linear fit result of $v_{2}$ - $v_{3}$ correlation within each centrality.

$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.

$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.

$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.

$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.

$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.

$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.

$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.

$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.

$v_{3}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{4}$ correlation within each centrality.

$v_{3}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{4}$ correlation within each centrality.

$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.

$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.

$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.

$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.

$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.

$v_5$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.

$v_5$ decomposed into linear and nonlinear contributions based on q3 event-shape selection.

RMS eccentricity scaled v_n.

RMS eccentricity scaled v_n.

$v_{2}$ - $v_{5}$ inclusive correlation in 5% centrality intervals.

$v_{2}$ - $v_{5}$ correlation for various q2 bins within each centrality.

$v_{3}$ - $v_{5}$ inclusive correlation in 5% centrality intervals.

$v_{3}$ - $v_{5}$ correlation for various q2 bins within each centrality.

Distribution of $v_{3}$ from MBT events plotted as a function of the A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of $v_{3}$ from $p_{T}^{jet}>75$ GeV events plotted as a function of the A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of $v_{3}$ from $p_{T}^{jet}>100$ GeV events plotted as a function of the A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of $v_{2}$ from MBT events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of $v_{2}$ from $p_{T}^{jet}>75$ GeV events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of $v_{2}$ from $p_{T}^{jet}>100$ GeV events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of $v_{2}$ from MBT events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of $v_{2}$ from $p_{T}^{jet}>75$ GeV events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of $v_{2}$ from $p_{T}^{jet}>100$ GeV events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of $v_{2}$ from MBT events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (9-100 GeV).

Distribution of $v_{2}$ from $p_{T}^{jet}>75$ GeV events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (9-100 GeV).

Distribution of $v_{2}$ from $p_{T}^{jet}>100$ GeV events plotted as a function of the event centrality and A-particle $p_\mathrm{T}$ in (9-100 GeV).

Distribution of relative UE-UE pair fractions from MBT events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative HS-HS pair fractions from MBT events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative HS-UE pair fractions from MBT events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative UE-HS pair fractions from MBT events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative UE-UE pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative HS-HS pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative HS-UE pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative UE-HS pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative UE-UE pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative HS-HS pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative HS-UE pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative UE-HS pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of A-particle $p_\mathrm{T}$ for 0-5% centrality.

Distribution of relative UE-UE pair fractions from MBT events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of relative HS-UE pair fractions from MBT events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of relative UE-UE pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of relative HS-UE pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of relative UE-UE pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of relative HS-UE pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (0.5-2 GeV).

Distribution of relative UE-UE pair fractions from MBT events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of relative HS-UE pair fractions from MBT events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of relative UE-UE pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of relative HS-UE pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of relative UE-UE pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of relative HS-UE pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (2-9 GeV).

Distribution of relative UE-UE pair fractions from MBT events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (9-100 GeV).

Distribution of relative HS-UE pair fractions from MBT events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (9-100 GeV).

Distribution of relative UE-UE pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (9-100 GeV).

Distribution of relative HS-UE pair fractions from $p_{T}^{jet}>75$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (9-100 GeV).

Distribution of relative UE-UE pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (9-100 GeV).

Distribution of relative HS-UE pair fractions from $p_{T}^{jet}>100$ GeV events plotted as a function of centrality A-particle $p_\mathrm{T}$ in (9-100 GeV).

Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m^{corr}_{\mu\mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the RPV SUSY model. Yellow and green stacked filled histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/30$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.

Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/4$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 125\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 125\ GeV$ is 7200 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 200\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 200 GeV$ is 840 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 350\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 350\ GeV$ is 50 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 700\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1150\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1600\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA to TMS association procedure, as a function of generated $L_{xy}$, in all HAHM signal samples combined.

Efficiencies of the Run 2 and Run 3 displaced dimuon triggers as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 50\ GeV$. The efficiency is defined as the fraction of simulated events that satisfy the requirements of the following sets of trigger paths: the Run 2 (2018) triggers (dashed black); the Run 3 (2022, L3) triggers (blue); the Run 3 (2022, L2) triggers (red); and the OR of all these triggers (Run 3 (2022), black). The lower panel shows the ratio of the overall Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 1\ cm$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 10\ m$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 20\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 50\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 125\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 200\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 350\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

The symmetric cumulant $sc_{2\,3}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The symmetric cumulant $sc_{2\,3}\{4\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The symmetric cumulant $sc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The symmetric cumulant $sc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The symmetric cumulant $sc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The symmetric cumulant $sc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The symmetric cumulant $sc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The symmetric cumulant $sc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The symmetric cumulant $sc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The asymmetric cumulant $ac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The asymmetric cumulant $ac_{2}\{3\}$results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The asymmetric cumulant $ac_{2}\{3\}$results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The asymmetric cumulant $ac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The asymmetric cumulant $ac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The asymmetric cumulant $ac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The normalized symmetric cumulant $nsc_{2\,3}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The normalized symmetric cumulant $nsc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The normalized asymmetric cumulant $nac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The normalized symmetric cumulant $nsc_{2\,3}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The normalized symmetric cumulant $nsc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The normalized asymmetric cumulant $nac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 13 TeV

The normalized symmetric cumulant $nsc_{2\,3}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The normalized symmetric cumulant $nsc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The normalized asymmetric cumulant $nac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The normalized symmetric cumulant $nsc_{2\,3}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The normalized symmetric cumulant $nsc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The normalized asymmetric cumulant $nac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The normalized symmetric cumulant $nsc_{2\,3}\{4\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The normalized symmetric cumulant $nsc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The normalized asymmetric cumulant $nac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The normalized symmetric cumulant $nsc_{2\,3}\{4\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The normalized symmetric cumulant $nsc_{2\,4}\{4\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The normalized asymmetric cumulant $nac_{2}\{3\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The $v_{2}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The $v_{3}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The $v_{4}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The $v_{2}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The $v_{3}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The $v_{4}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The $v_{2}\{2\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The $v_{3}\{2\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The $v_{4}\{2\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The $v_{2}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The $v_{3}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The $v_{4}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pp collisions at $\sqrt{s_{NN}}$ = 13 TeV

The $v_{2}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The $v_{3}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The $v_{4}\{2\}$ results as a function of multiplicity ($N_{ch}$) in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The $v_{2}\{2\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The $v_{3}\{2\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The $v_{4}\{2\}$ results as a function of multiplicity ($N_{ch}$) in PbPb collisions at $\sqrt{s_{NN}}$ = 2.76 TeV

The symmetric cumulant $ac_{2}\{3\}$ in Pb+Pb from different methods

The symmetric cumulant $ac_{2}\{3\}$ in Pb+Pb from different methods

The symmetric cumulant $ac_{2}\{3\}$ in p+Pb from different methods

The symmetric cumulant $ac_{2}\{3\}$ in p+Pb from different methods

The symmetric cumulant $ac_{2}\{3\}$ in pp from different methods

The symmetric cumulant $ac_{2}\{3\}$ in pp from different methods

Comparison of the $m_{miss}$ shapes for the simulated signal events within the fiducial region and those outside it, after including the effect of PU protons as describe in the text, for a generated $m_{X}$ mass of 1000 GeV. The distributions are shown for single(+z)-single(−z) proton reconstruction categories.

Product of the acceptance times the combined reconstruction and identification efficiency, as a function of $m_{X}$ , for events generated inside the fiducial volume defined in Table 1. The curves shown panel display the different final states. The definition of the fiducial region and signal model used to estimate the acceptance is provided in the text.

Product of the acceptance times the combined reconstruction and identification efficiency, as a function of $m_{X}$ , for different proton reconstruction categories in the $Z \rightarrow \mu\mu $ analysis, and for events generated inside the fiducial volume defined in Table 1. The curves shown panel display the different final states. The definition of the fiducial region and signal model used to estimate the acceptance is provided in the text.

Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The proton $\xi$ distribution for the positive CT-PPS arm is shown.

Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The proton $\xi$ distribution for the negative CT-PPS arm is shown.

Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The di-proton invariant mass is shown.

Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The $m_{miss}$ is shown.

$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.

$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.

$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.

$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.

Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z\rightarrow ee$ analysis.

Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z\rightarrow \mu\mu$ analysis.

Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z$ analysis.

Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $\gamma$ analysis.