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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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 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 $g_{q}$ 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 $g_{q}$ 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 pseudoscalar couplings.

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.

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 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.

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.

The correlation matrix for the Higgs pT measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The correlation matrix for the jet multiplicity measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The correlation matrix for the leading jet pT measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The fiducial integrated signal strength and cross section.

${p}_{T}^{miss}$ distribution of data and MC events in the signal region with the signal stacked on top of the background prediction for a mass hypothesis of ${m}_{stop} = 225GeV$ and ${m}_{LSP} = 50 GeV$. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio. The uncertainty band includes statistical, background normalization and all systematic uncertainties.

${p}_{T}^{miss}$ normalized distribution for signal points with different top squark and neutralino masses and SM ttbar events are compared.

${m}_{T2} (e\mu)$ normalized distribution for signal points with different top squark and neutralino masses and SM ttbar events are compared.

$\Delta\eta (e\mu)$ normalized distribution for signal points with different top squark and neutralino masses and SM ttbar events are compared.

$\Delta\phi (e\mu)$ normalized distribution for signal points with different top squark and neutralino masses and SM ttbar events are compared.

Normalized DNN score distribution in the signal region for two mass hypotheses to compare signal and ttbar background: $m_{LSP}=$ 50 (100) GeV and $m_{stop}=$ 225 (275) GeV.

Post-fit DNN score distributions in the signal region with the signal superimposed and scaled by a factor of 20 for different mass hypotheses of $({m}_{stop}, {m}_{LSP})=$ (225, 50) GeV. The uncertainty band includes statistical, background normalization, and all systematic uncertainties. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio before the fit (line) and after (dots), each of them with their corresponding band of uncertainties, and the ratio between the sum of the signal and background predictions and the background prediction. The masses of the signal model correspond to the values of the DNN mass parameters in each distribution.

Post-fit DNN score distributions in the signal region with the signal superimposed and scaled by a factor of 20 for different mass hypotheses of $({m}_{stop}, {m}_{LSP})=$ (275, 100) GeV. The uncertainty band includes statistical, background normalization, and all systematic uncertainties. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio before the fit (line) and after (dots), each of them with their corresponding band of uncertainties, and the ratio between the sum of the signal and background predictions and the background prediction. The masses of the signal model correspond to the values of the DNN mass parameters in each distribution.

Post-fit DNN score distributions in the signal region with the signal superimposed and scaled by a factor of 20 for different mass hypotheses of $({m}_{stop}, {m}_{LSP})=$ (275, 70) GeV. The uncertainty band includes statistical, background normalization, and all systematic uncertainties. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio before the fit (line) and after (dots), each of them with their corresponding band of uncertainties, and the ratio between the sum of the signal and background predictions and the background prediction. The masses of the signal model correspond to the values of the DNN mass parameters in each distribution.

Post-fit DNN score distributions in the signal region with the signal superimposed and scaled by a factor of 20 for different mass hypotheses of $({m}_{stop}, {m}_{LSP})=$ (245, 100) GeV. The uncertainty band includes statistical, background normalization, and all systematic uncertainties. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio before the fit (line) and after (dots), each of them with their corresponding band of uncertainties, and the ratio between the sum of the signal and background predictions and the background prediction. The masses of the signal model correspond to the values of the DNN mass parameters in each distribution.

Upper limit at 95% CL on the signal cross section as a function of the top squark and neutralino masses. The model is excluded for all of the colored region inside the black boundary.

Upper limit at 95% CL on the signal cross section as a function of the top squark mass for mass difference of 175 GeV. The green and yellow bands represent the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The purple dotted line indicates the approximate NNLO + NNLL productioncross section.

Upper limit at 95% CL on the signal cross section as a function of the top squark mass for mass difference of 185 GeV. The green and yellow bands represent the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The purple dotted line indicates the approximate NNLO + NNLL productioncross section.

Upper limit at 95% CL on the signal cross section as a function of the top squark mass for mass difference of 165 GeV. The green and yellow bands represent the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The purple dotted line indicates the approximate NNLO + NNLL productioncross section.

Summary of the contributions of the experimental uncertainties in the DNN score distribution for signal and the ttbar background. The values represent the relative variation in the number of expected events across different signal models in the signal region.

Summary of the contribution of each modeling uncertainty source to the DNN score distribution for the ttbar background.

95% CL expected upper limit on top squark cross section for the direct decay model.

95% CL observed upper limit on top squark cross section for the direct decay model.

Contour of the region excluded with 95% CL (expected).

Contour of the region excluded with 95% CL (observed).

95% CL expected upper limit on top squark cross section for the cascade decay model.

95% CL observed upper limit on top squark cross section for the cascade decay model.

Contour of the region excluded with 95% CL (expected).

Contour of the region excluded with 95% CL (observed).

95% CL expected upper limit on top squark cross section for the mixed decay model.

95% CL observed upper limit on top squark cross section for the mixed decay model.

Contour of the region excluded with 95% CL (expected).

Contour of the region excluded with 95% CL (observed).

95% CL expected limit on $\sigma/\sigma_{theory}$ for a fermionic DM particle with m = 1 GeV, as a function of the mediator mass for a scalar.

95% CL observed limit on $\sigma/\sigma_{theory}$ for a fermionic DM particle with m = 1 GeV, as a function of the mediator mass for a scalar.

95% CL expected limit on $\sigma/\sigma_{theory}$ for a fermionic DM particle with m = 1 GeV, as a function of the mediator mass for a pseudoscalar.

95% CL observed limit on $\sigma/\sigma_{theory}$ for a fermionic DM particle with m = 1 GeV, as a function of the mediator mass for a pseudoscalar.

Differential cross section as a function of $m_{\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $p_{T,\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $a_{T,\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $\phi_{\eta}*$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $\pi-\Delta\phi_{\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $|cos\theta*|^{(CS)}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

$v_3$ vs $p_T$, p+Au at 200 GeV, 0-5% central, BBCS-FVTXS-CNT detector combination

$v_3$ vs $p_T$, d+Au at 200 GeV, 0-5% central, BBCS-FVTXS-CNT detector combination

$v_3$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, BBCS-FVTXS-CNT detector combination

$v_2$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_2$ vs $p_T$, d+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_2$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_2$ ratio vs $p_T$, p/d/3He+Au at 200 GeV, 0-5% central

$v_3$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_3$ vs $p_T$, d+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_3$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$c_2$ vs $\eta$, p+Au at 200 GeV, 0-5% central

$c_2$ vs $\eta$, d+Au at 200 GeV, 0-5% central

$c_2$ vs $\eta$, 3He+Au at 200 GeV, 0-5% central

$c_3$ vs $\eta$, p+Au at 200 GeV, 0-5% central

$c_3$ vs $\eta$, d+Au at 200 GeV, 0-5% central

$c_3$ vs $\eta$, 3He+Au at 200 GeV, 0-5% central

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, BBCS-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, BBCS-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, BBCS-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, BBCS-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, BBCS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, BBCS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, BBCS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, BBCS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, FVTXS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, FVTXS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, FVTXS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, FVTXS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, CNT-BBCS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, CNT-BBCS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, CNT-BBCS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, CNT-BBCS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, CNT-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, CNT-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, CNT-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, CNT-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, CNT-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, CNT-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, CNT-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, CNT-FVTXN correlation

$c_n$ vs detector combination, p+p at 200 GeV, Minimum Bias

$c_n$ vs $p_T$, p+p at 200 GeV, Minimum Bias, BBCS-CNT detector combination

$c_n$ vs $p_T$, p+p at 200 GeV, Minimum Bias, FVTXS-CNT detector combination

$c_n$ vs $p_T$, p+p at 200 GeV, Minimum Bias, FVTXN-CNT detector combination

$c_n$ vs detector combination, p+Au at 200 GeV, 0-5% central

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, BBCS-CNT detector combination

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXS-CNT detector combination

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXN-CNT detector combination

$c_n$ vs detector combination, p+Au at 200 GeV, 0-5% central

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, BBCS-CNT detector combination

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXS-CNT detector combination

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXN-CNT detector combination

$c_n$ vs detector combination, p+Au at 200 GeV, 0-5% central

$c_n$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, BBCS-CNT detector combination

$c_n$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, FVTXS-CNT detector combination

$c_n$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, FVTXN-CNT detector combination