The average $(\Lambda + \overline\Lambda)$ polarization along beam direction as function of hyperon rapidity for 30-50% centrality in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$~TeV.

$\Delta\varphi$ projection of h-h correlation function with $3<p_{\mathrm{T}}^{\mathrm{trigg}}< 4 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of $K_S^0$-h correlation function with $3<p_{\mathrm{T}}^{\mathrm{trigg}}< 4 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of $K_S^0$-h correlation function with $3<p_{\mathrm{T}}^{\mathrm{trigg}}< 4 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of ($\Lambda+\overline{\Lambda}$)-h correlation function with $3<p_{\mathrm{T}}^{\mathrm{trigg}}< 4 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of ($\Lambda+\overline{\Lambda}$)-h correlation function with $3<p_{\mathrm{T}}^{\mathrm{trigg}}< 4 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of h-h correlation function with $9<p_{\mathrm{T}}^{\mathrm{trigg}}< 11 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of h-h correlation function with $9<p_{\mathrm{T}}^{\mathrm{trigg}}< 11 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of $K_S^0$-h correlation function with $9<p_{\mathrm{T}}^{\mathrm{trigg}}< 11 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of $K_S^0$-h correlation function with $9<p_{\mathrm{T}}^{\mathrm{trigg}}< 11 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of ($\Lambda+\overline{\Lambda}$)-h correlation function with $9<p_{\mathrm{T}}^{\mathrm{trigg}}< 11 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

$\Delta\varphi$ projection of ($\Lambda+\overline{\Lambda}$)-h correlation function with $9<p_{\mathrm{T}}^{\mathrm{trigg}}< 11 \mathrm{GeV}/c$ and $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Per-trigger yields of ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side with $1 \mathrm{GeV}/c<p_{\mathrm{T}}^{\mathrm{assoc}}< p_{\mathrm{T}}^{\mathrm{trigg}} $

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side

Ratio of the per-trigger yields in different multiplicity classes to the corresponding minimum bias yield for ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side

Per-trigger yields of h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side from minimum bias

Per-trigger yields of h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side from minimum bias

Per-trigger yields of $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side from minimum bias

Per-trigger yields of $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side from minimum bias

Per-trigger yields of ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side from minimum bias

Per-trigger yields of ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side from minimum bias

Per-trigger yields of h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side from minimum bias

Per-trigger yields of h-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side from minimum bias

Per-trigger yields of $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side from minimum bias

Per-trigger yields of $K_S^0$-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side from minimum bias

Per-trigger yields of ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side from minimum bias

Per-trigger yields of ($\Lambda+\overline{\Lambda}$)-h correlation functions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side from minimum bias

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for minimum bias

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for minimum bias

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 0-1% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 0-1% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 1-3% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 1-3% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 3-7% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 3-7% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 7-15% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 7-15% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 15-50% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 15-50% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 50-100% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 50-100% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for minimum bias

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for minimum bias

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 0-1% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 0-1% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 1-3% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 1-3% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 3-7% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 3-7% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 7-15% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 7-15% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 15-50% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 15-50% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 50-100% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side for 50-100% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for minimum bias

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for minimum bias

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 0-1% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 0-1% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 1-3% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 1-3% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 3-7% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 3-7% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 7-15% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 7-15% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 15-50% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 15-50% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 50-100% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 50-100% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for minimum bias

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for minimum bias

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 0-1% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 0-1% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 1-3% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 1-3% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 3-7% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 3-7% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 7-15% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 7-15% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 15-50% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 15-50% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 50-100% multiplicity class

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the away-side for 50-100% multiplicity class

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $3 < p_{\mathrm{T}}^{\mathrm{trigg}} < 4 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $3 < p_{\mathrm{T}}^{\mathrm{trigg}} < 4 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $4 < p_{\mathrm{T}}^{\mathrm{trigg}} < 5 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $4 < p_{\mathrm{T}}^{\mathrm{trigg}} < 5 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $5 < p_{\mathrm{T}}^{\mathrm{trigg}} < 6 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $5 < p_{\mathrm{T}}^{\mathrm{trigg}} < 6 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $6 < p_{\mathrm{T}}^{\mathrm{trigg}} < 7 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $6 < p_{\mathrm{T}}^{\mathrm{trigg}} < 7 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $7 < p_{\mathrm{T}}^{\mathrm{trigg}} < 9 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $7 < p_{\mathrm{T}}^{\mathrm{trigg}} < 9 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $9 < p_{\mathrm{T}}^{\mathrm{trigg}} < 11 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $9 < p_{\mathrm{T}}^{\mathrm{trigg}} < 11 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $3 < p_{\mathrm{T}}^{\mathrm{trigg}} < 4 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $3 < p_{\mathrm{T}}^{\mathrm{trigg}} < 4 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $4 < p_{\mathrm{T}}^{\mathrm{trigg}} < 5 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $4 < p_{\mathrm{T}}^{\mathrm{trigg}} < 5 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $5 < p_{\mathrm{T}}^{\mathrm{trigg}} < 6 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $5 < p_{\mathrm{T}}^{\mathrm{trigg}} < 6 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $6 < p_{\mathrm{T}}^{\mathrm{trigg}} < 7 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $6 < p_{\mathrm{T}}^{\mathrm{trigg}} < 7 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $7 < p_{\mathrm{T}}^{\mathrm{trigg}} < 9 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $7 < p_{\mathrm{T}}^{\mathrm{trigg}} < 9 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $9 < p_{\mathrm{T}}^{\mathrm{trigg}} < 11 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $9 < p_{\mathrm{T}}^{\mathrm{trigg}} < 11 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side for $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $3 < p_{\mathrm{T}}^{\mathrm{trigg}} < 4 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $3 < p_{\mathrm{T}}^{\mathrm{trigg}} < 4 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $4 < p_{\mathrm{T}}^{\mathrm{trigg}} < 5 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $4 < p_{\mathrm{T}}^{\mathrm{trigg}} < 5 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $5 < p_{\mathrm{T}}^{\mathrm{trigg}} < 6 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $5 < p_{\mathrm{T}}^{\mathrm{trigg}} < 6 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $6 < p_{\mathrm{T}}^{\mathrm{trigg}} < 7 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $6 < p_{\mathrm{T}}^{\mathrm{trigg}} < 7 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $7 < p_{\mathrm{T}}^{\mathrm{trigg}} < 9 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $7 < p_{\mathrm{T}}^{\mathrm{trigg}} < 9 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $9 < p_{\mathrm{T}}^{\mathrm{trigg}} < 11 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $9 < p_{\mathrm{T}}^{\mathrm{trigg}} < 11 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of $K_S^0$-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $3 < p_{\mathrm{T}}^{\mathrm{trigg}} < 4 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $3 < p_{\mathrm{T}}^{\mathrm{trigg}} < 4 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $4 < p_{\mathrm{T}}^{\mathrm{trigg}} < 5 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $4 < p_{\mathrm{T}}^{\mathrm{trigg}} < 5 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $5 < p_{\mathrm{T}}^{\mathrm{trigg}} < 6 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $5 < p_{\mathrm{T}}^{\mathrm{trigg}} < 6 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $6 < p_{\mathrm{T}}^{\mathrm{trigg}} < 7 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $6 < p_{\mathrm{T}}^{\mathrm{trigg}} < 7 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $7 < p_{\mathrm{T}}^{\mathrm{trigg}} < 9 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $7 < p_{\mathrm{T}}^{\mathrm{trigg}} < 9 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $9 < p_{\mathrm{T}}^{\mathrm{trigg}} < 11 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $9 < p_{\mathrm{T}}^{\mathrm{trigg}} < 11 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

Ratios of integrated per-trigger yield of ($\Lambda+\overline{\Lambda}$)-h to h-h as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the away-side for $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.400\pm0.056~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.597\pm0.057~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.793\pm0.057~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.992\pm0.058~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.189\pm0.058~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.435\pm0.086~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.761\pm0.111~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

Groomed jet momentum splitting fraction $z_{{\mathrm{g}}}$ in pp collisions. $80<p_{\mathrm{T,\;ch\;jet}}<100 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction. For the "trkeff" and "generator" systematic uncertainty sources, the signed systematic uncertainty breakdowns ($\pm$ vs. $\mp$), denote correlation across bins (both within this table, and across tables for a given centrality). For the remaining sources ("unfolding") no correlation information is specified ($\pm$ is always used).

Groomed jet momentum splitting fraction $z_{{\mathrm{g}}}$ in Pb-Pb collisions. $80<p_{\mathrm{T,\;ch\;jet}}<100 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet momentum splitting fraction $z_{{\mathrm{g}}}$ $-$ ratio of Pb-Pb to pp collisions. $80<p_{\mathrm{T,\;ch\;jet}}<100 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ in pp collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction. For the "trkeff" and "generator" systematic uncertainty sources, the signed systematic uncertainty breakdowns ($\pm$ vs. $\mp$), denote correlation across bins (both within this table, and across tables for a given centrality). For the remaining sources ("unfolding") no correlation information is specified ($\pm$ is always used).

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ in Pb-Pb collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ $-$ ratio of Pb-Pb to pp collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet momentum splitting fraction $z_{{\mathrm{g}}}$ in pp collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction. For the "trkeff" and "generator" systematic uncertainty sources, the signed systematic uncertainty breakdowns ($\pm$ vs. $\mp$), denote correlation across bins (both within this table, and across tables for a given centrality). For the remaining sources ("unfolding") no correlation information is specified ($\pm$ is always used).

Groomed jet momentum splitting fraction $z_{{\mathrm{g}}}$ in Pb-Pb collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet momentum splitting fraction $z_{{\mathrm{g}}}$ $-$ ratio of Pb-Pb to pp collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ in pp collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction. For the "trkeff" and "generator" systematic uncertainty sources, the signed systematic uncertainty breakdowns ($\pm$ vs. $\mp$), denote correlation across bins (both within this table, and across tables for a given centrality). For the remaining sources ("unfolding") no correlation information is specified ($\pm$ is always used).

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ in Pb-Pb collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ $-$ ratio of Pb-Pb to pp collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.2, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ in pp collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.4, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction. For the "trkeff" and "generator" systematic uncertainty sources, the signed systematic uncertainty breakdowns ($\pm$ vs. $\mp$), denote correlation across bins (both within this table, and across tables for a given centrality). For the remaining sources ("unfolding") no correlation information is specified ($\pm$ is always used).

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ in Pb-Pb collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.4, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Groomed jet radius (scaled) $\theta_{{\mathrm{g}}}$ $-$ ratio of Pb-Pb to pp collisions. $60<p_{\mathrm{T,\;ch\;jet}}<80 \;\mathrm{GeV}/c$, Soft Drop $z_{\mathrm{cut}}=0.4, \beta=0$. Note: The first bin corresponds to the Soft Drop untagged fraction.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

Correlation for the uncertainties of the differential cross-section of prompt inclusive production of long-lived charged particles.

Correlation for the uncertainties of the differential cross-section of prompt inclusive production of long-lived charged particles.

Correlation for the uncertainties of the differential cross-section of prompt inclusive production of long-lived charged particles.

Correlation between the Wilson coefficients (in %), after the 5D fit to data.

The measured differential branching fractions as a function of the invariant hadronic mass squared of the $X_u$ system ($M_X^2$).

The measured differential branching fractions as a function of the light-cone momenta of the hadronic $X_u$ system [$P_+ = (E_X^B - |old{p}_X^B|)$].

The measured differential branching fractions as a function of the light-cone momenta of the hadronic $X_u$ system [$P_- = (E_X^B + |old{p}_X^B|)$].

The background-subtracted yields as a function of $E_\ell^B$.

The background-subtracted yields as a function of $q^2$.

The background-subtracted yields as a function of $M_X$.

The background-subtracted yields as a function of $M_X^2$.

The background-subtracted yields as a function of $P_+$. [$P_+ = (E_X^B - |P_X^B|)$]

The background-subtracted yields as a function of $P_-$. [$P_- = (E_X^B + |P_X^B|)$].

The full experimental correlation matrix for the background-subtracted spectrum of the lepton energy in the $B$ rest frame ($E_\ell^B$).

The full experimental correlation matrix for the background-subtracted spectrum of $q^2$).

The full experimental correlation matrix for the background-subtracted spectrum of $M_X$.

The full experimental correlation matrix for the background-subtracted spectrum of $M_X^2$.

The full experimental correlation matrix for the background-subtracted spectrum of $P_+$.

The full experimental correlation matrix for the background-subtracted spectrum of $P_-$.

Migration matrix of $E_\ell^B$ [in %].

Migration matrix of $q^2$ [in %].

Migration matrix of $M_X$ [in %].

Migration matrix of $M_X^2$ [in %].

Migration matrix of $P_+$ [in %].

Migration matrix of $P_-$ [in %].

The total correction factors and phase space acceptance as a function of the invariant hadronic mass of the $X_u$ system ($M_X$).

The total correction factors and phase space acceptance as a function of the invariant hadronic mass squared of the $X_u$ system ($M_X^2$).

The total correction factors and phase space acceptance as a function of the lepton energy in the $B$ rest frame ($E_\ell^B$).

The total correction factors and phase space acceptance as a function of the four-momentum-transfer squared of the $B$ to the $X_u$ system ($q^2$).

The total correction factors and phase space acceptance as a function of $P_+$.

The total correction factors and phase space acceptance as a function of $P_-$.

The statistical correlations of the differential branching fractions are shown. The matrix elements are ordered for $M_{X}$, $M_{X}^{2}$, $q^{2}$, $E_{\ell}^{B}$, $P_{+}$ and $P_{-}$.

The full experimental (statistical and systematical) correlations of the differential branching fractions are shown. The matrix elements are ordered for $M_{X}$, $M_{X}^{2}$, $q^{2}$, $E_{\ell}^{B}$, $P_{+}$ and $P_{-}$.

The total partial branching fraction with $E_{\ell}^{B} > 1$ GeV. The order of entries is: the 2D fit result of Phys. Rev. D 104, 012008 (2021), the results calcuated with the differential branching fractions of $M_{X}$, $M_{X}^{2}$, $q^{2}$, $E_{\ell}^{B}$, $P_{+}$ and $P_{-}$

The full experimental correlations between the total partial branching fractions from summing the individual bins are shown. The matrix elements are saved in the order of $M_{X}$, $M_{X}^{2}$, $q^{2}$, $E_{\ell}^{B}$, $P_{+}$ and $P_{-}$.

The first moment of the measured differential branching fraction of the lepton energy in the $B$ rest frame ($E_\ell^B$).

The second moment of the measured differential branching fraction of the lepton energy in the $B$ rest frame ($E_\ell^B$).

The third moment of the measured differential branching fraction of the lepton energy in the $B$ rest frame ($E_\ell^B$).

The first moment of the measured differential branching fraction of $q^2$.

The second moment of the measured differential branching fraction of $q^2$.

The third moment of the measured differential branching fraction of $q^2$.

The first moment of the measured differential branching fraction of $M_{X}$.

The second moment of the measured differential branching fraction of $M_{X}$.

The third moment of the measured differential branching fraction of $M_{X}$.

The first moment of the measured differential branching fraction of $P_{+}$.

The second moment of the measured differential branching fraction of $P_{+}$.

The third moment of the measured differential branching fraction of $P_{+}$.

The first moment of the measured differential branching fraction of $P_{-}$.

The second moment of the measured differential branching fraction of $P_{-}$.

The third moment of the measured differential branching fraction of $P_{-}$.

The matrix elements are saved in the order of $M_{X}$, $q^{2}$, $E_{\ell}^{B}$, $P_{+}$ and $P_{-}$. For each variable, the entries is ordered for the first, second and third moments.

The centrality dependence of Global $\Lambda$-hyperon Polarization at $\sqrt{s_{\rm NN}}=3$ GeV.

The $p_{\rm T}$ dependence of Global $\Lambda$-hyperon Polarization at $\sqrt{s_{\rm NN}}=3$ GeV.

The rapidity dependence of Global $\Lambda$-hyperon Polarization at $\sqrt{s_{\rm NN}}=3$ GeV.

Rapidity($y$) dependence of $v_1$ (top panels) and $v_2$ (bottom panels) of proton and $\Lambda$ baryons (left panels), pions (middle panels) and kaons (right panels) in 10-40% centrality for the $\sqrt{s_{NN}}$ = 3GeV Au+Au collisions. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The UrQMD and JAM results are shown as bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. The value of the incompressibility $\kappa$ = 380 MeV is used in the mean-field option. More detailed model descriptions and data comparisons can be found in Supplemental Material.

Rapidity($y$) dependence of $v_1$ (top panels) and $v_2$ (bottom panels) of proton and $\Lambda$ baryons (left panels), pions (middle panels) and kaons (right panels) in 10-40% centrality for the $\sqrt{s_{NN}}$ = 3GeV Au+Au collisions. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The UrQMD and JAM results are shown as bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. The value of the incompressibility $\kappa$ = 380 MeV is used in the mean-field option. More detailed model descriptions and data comparisons can be found in Supplemental Material.

Rapidity($y$) dependence of $v_1$ (top panels) and $v_2$ (bottom panels) of proton and $\Lambda$ baryons (left panels), pions (middle panels) and kaons (right panels) in 10-40% centrality for the $\sqrt{s_{NN}}$ = 3GeV Au+Au collisions. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The UrQMD and JAM results are shown as bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. The value of the incompressibility $\kappa$ = 380 MeV is used in the mean-field option. More detailed model descriptions and data comparisons can be found in Supplemental Material.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

$v_2$ scaled by the number of constituent quarks, $v_2/n_q$ , as a function of scaled transverse kinetic energy ($(m_T − m_0)/n_q$) for pions, kaons and protons from Au+Au collisions in 10-40% centrality at $\sqrt{s_{NN}}$ = 3, 27, and 54.4 GeV for positive charged particles (left panel) and negative charged particles (right panel). Colored dashed lines represent the scaling fit to data in 7.7, 14.5, 27, 54.4, and 200 GeV Au+Au collisions from STAR experiment at RHIC [43–45]. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.

Collision energy dependence of (top panel) directed flow slope $dv_1/dy|_{y=0}$ for p, $\Lambda, \pi$(combined from $\pi^{\pm}$), K(combined from $K^{\pm}$ and $K_S^0$), $\phi$ and $\Xi^−$, and (bottom panel) elliptic flow $v_2$ for p, $\pi$(combined from $\pi^{\pm}$) in heavy- ion collisions. Collision centrality for all data from RHIC is 10-40%, except for 4.5 GeV where 10-30% is for $dv_1/dy|_{y=0}$ and 0-30% is for $v_2$. Statistical and systematic uncertainties are shown as bars and gray bands, respectively. Some uncertainties are smaller than the data points. The JAM and UrQMD results are shown as colored bands:golden, red and blue bands stand for JAM mean-field, UrQMD mean-field and UrQMD cascade mode, respectively. For clarity the x-axis value of the data points have been shifted.