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

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