Joint Bayesian analysis of soft and high-p probes yields tighter constraints on QGP properties
Abstract
To extract bulk QGP properties, we perform a joint Bayesian calibration of bulk-medium parameters using low- bulk and high- tomography within a common medium evolution. Low- observables are computed with TRENTo+VISHNU; temperature profiles are passed to DREENA-A to predict light/heavy RAA() and v2(). Gaussian-process emulation enables Hamiltonian Monte Carlo sampling of the low--only and joint posteriors. The low--only case underpredicts high- anisotropy; the joint calibration matches both sectors and markedly tightens bulk-parameter constraints, demonstrating the added power of high- data.
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