Joint Bayesian analysis of soft and high-$p_\perp$ probes yields tighter constraints on QGP properties

TL;DR

This paper demonstrates that combining low- and high-$p_T$ data in a Bayesian framework improves constraints on quark-gluon plasma properties, using advanced modeling and emulation techniques.

Contribution

It introduces a joint Bayesian calibration method that integrates low- and high-$p_T$ observables for more precise QGP property extraction.

Findings

Joint calibration tightens bulk-parameter constraints.

High-$p_T$ data improves model accuracy.

Low-$p_T$ only underpredicts high-$p_T$ anisotropy.

Abstract

To extract bulk QGP properties, we perform a joint Bayesian calibration of bulk-medium parameters using low-$\pt$ bulk and high-$\pt$ tomography within a common medium evolution. Low-$\pt$ observables are computed with \textsc{TRENTo}+\textsc{VISHNU}; temperature profiles are passed to \textsc{DREENA-A} to predict light/heavy $R_{\mathrm{AA}}(\pt)$ and $v_2(\pt)$. Gaussian-process emulation enables Hamiltonian Monte Carlo sampling of the low-$\pt$-only and joint posteriors. The low-$\pt$-only case underpredicts high-$\pt$ anisotropy; the joint calibration matches both sectors and markedly tightens bulk-parameter constraints, demonstrating the added power of high-$\pt$ data.