Speakers
Description
This study presents the first application of Bayesian inference to a hybrid model that combines hadronic transport as an initial state with a shear and bulk viscosity dependent on both temperature and baryochemical potential. Typically, the extraction of nuclear matter properties, such as viscosities, from experimental data involves Bayesian inference on theoretical models with parametric initial conditions, which leads to high-dimensional priors and reduces the strength of constraints. By integrating the SMASH transport code with the vHLLE hydrodynamic framework and a chiral mean field model equation of state in the (3+1)D SMASH-vHLLE-hybrid \textsuperscript{1} model, applied within the RHIC Beam Energy Scan (BES) range of $\sqrt{s_{NN}}$ = 7.7 - 200, we reduce reliance on parametric assumptions, while leveraging a physically motivated initial state.
Despite the use of hadronic transport for initial conditions, some parametric uncertainty remains. This study offers new insights into the impact of initial state smearing and the lifetime of the pre-equilibrium state on observables. In particular, we provide updated constraints on the temperature and baryochemical potential dependencies of the shear and bulk viscosity. We also investigate the effect of the hydrodynamic phase's lifetime on observables and the role of cross-sections in the final state rescattering. Our analysis covers a diverse range of experimental data, including both midrapidity and rapidity-dependent measurements, to assess the impact of model parameters on the robustness of the inferred results.
References
[1] https://github.com/smash-transport/smash-vhlle-hybrid
| Category | Theory |
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