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.

No description provided.

95% CL expected upper limit on chargino/neutralino cross section.

95% CL expected upper limit on chargino/neutralino cross section.

95% CL expected upper limit on chargino/neutralino cross section.

95% CL Observed upper limit on chargino/neutralino cross section.

95% CL Observed upper limit on chargino/neutralino cross section.

95% CL Observed upper limit on chargino/neutralino cross section.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.

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.

Jet angularity $\lambda_{\alpha}$ for $\alpha = 3$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1.5$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 2$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 3$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1.5$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 2$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 3$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1.5$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 2$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 3$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1.5$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 2$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 3$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1.5$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 2$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 3$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1.5$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 2$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 3$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1.5$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 2$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 3$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1.5$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 2$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 3$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1.5$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 2$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 3$. $20<p_{\mathrm{T}}^{\mathrm{ch jet}}<40$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1.5$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 2$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 3$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1.5$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 2$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 3$. $40<p_{\mathrm{T}}^{\mathrm{ch jet}}<60$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1.5$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 2$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 3$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1.5$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 2$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 3$. $60<p_{\mathrm{T}}^{\mathrm{ch jet}}<80$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 1.5$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 2$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Jet angularity $\lambda_{\alpha}$ for $\alpha = 3$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$. 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 1.5$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 2$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

Groomed jet angularity $\lambda_{\alpha,g}$ for $\alpha = 3$. $80<p_{\mathrm{T}}^{\mathrm{ch jet}}<100$, 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 the remaining sources ("unfolding", "random_mass") no correlation information is specified ($\pm$ is always used).

$\Delta\varphi$ 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 $11 < p_{\mathrm{T}}^{\mathrm{trigg}} < 15 \mathrm{GeV}/c$

${p}_{T}^{miss}$ 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.

Pb-remnant side ZN signal normalized to MB value vs. ZN centrality percentile in p-Pb collisions at 8.16 TeV

p-remnant side ZN signal normalized to MB value vs. ZN centrality percentile in p-Pb collisions at 5.02 TeV

p-remnant side ZN signal normalized to MB value vs. ZN centrality percentile in p-Pb collisions at 8.16 TeV

Pb-remnant side ZN signal normalized to MB value vs. average Ncoll in p-Pb collisions at 5.02 TeV

Pb-remnant side ZN signal normalized to MB value vs. average Ncoll in p-Pb collisions at 8.16 TeV

p-remnant side ZN signal normalized to MB value vs. average Ncoll in p-Pb collisions at 5.02 TeV

p-remnant side ZN signal normalized to MB value vs. average Ncoll in p-Pb collisions at 8.16 TeV

ZN signal normalized to MB value vs. normalized charged-particle multiplicity in p-p collisions at 13 TeV

p-remnant side ZN signal normalized to MB value vs. normalized charged-particle multiplicity in p-Pb collisions at 8.16 TeV

ZP signal normalized to MB value vs. normalized charged-particle multiplicity in p-p collisions at 13 TeV

p-remnant side ZP signal normalized to MB value vs. average charged-particle multiplicity in p-Pb collisions at 8.16 TeV

ZN signal normalized to MB value vs. leading transverse momentum in |eta|<0.8 in p-p collisions at 13 TeV

ZP signal normalized to MB value vs. leading transverse momentum in |eta|<0.8 in p-p collisions at 13 TeV

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.

95% CL exclusion limits on $\mathcal{B}(H\rightarrow aa \rightarrow b\bar{b}b\bar{b})$ for $m_a = 45$ GeV.

95% CL exclusion limits on $\mathcal{B}(H\rightarrow aa \rightarrow b\bar{b}b\bar{b})$ for $m_a = 55$ GeV.

The fraction of $a$ boson decays matched to reconstructed displaced vertices passing all vertex selections in signal MC.

The extrapolated signal selection efficiency as a function of $c\tau_{a}$.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The cluster efficiency in bins of hadronic and EM energy in region A. Region A is defined as 391 cm $< r <$ 695.5 cm and 400 cm $< |z| <$ 671 cm. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The cluster efficiency in bins of hadronic and EM energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The efficiency of $N_{station} > 1$ requirement in bins of hadronic energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bin corresponds to LLPs that decayed leptonically with 0 hadronic energy. The efficiency is calculated with respect to clusters that pass all cluster-level cuts described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 10%.

The geometric signal acceptance as a function of $c\tau$. The acceptance is shown for four different LLP mass hypotheses: 7, 15, 40, and 55 GeV. The acceptance is defined by requiring at least 1 LLP to decay in the region defined as 400 cm $< |z| <$ 1100 cm, $r < $ 695.5 cm, and $|\eta| <$ 2.4.