Bayesian Analyses of Proton Multiple Flow Components in Intermediate Heavy Ion Collisions with Momentum-Dependent Interactions
Shuochong Han, Ang Li

TL;DR
This study uses Bayesian analysis with a transport model to extract nuclear matter properties from heavy-ion collision data, emphasizing the importance of momentum-dependent interactions in constraining the nuclear equation of state.
Contribution
It introduces a Bayesian framework with Gaussian Process emulators to simultaneously constrain the incompressibility and in-medium baryon-baryon scattering modifications from experimental flow data.
Findings
Incompressibility favors a soft nuclear equation of state.
In-medium baryon-baryon cross sections are mildly suppressed.
Momentum dependence in the mean field is crucial for accurate modeling.
Abstract
We perform a comprehensive Bayesian analyses of Au + Au collision data at 1.23 GeV/nucleon using an isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model that incorporates a momentum-dependent mean field and medium-modified baryon-baryon cross sections. The model parameters are calibrated to empirical properties of nuclear matter at saturation density, with particular attention to variations in the incompressibility . Within a Bayesian statistical framework and using a Gaussian Process emulator, we simultaneously extract constraints on the incompressibility and the in-medium baryon-baryon scattering modification factor by systematically comparing model predictions with HADES measurements of proton collective flow, including the slopes ( and ) of directed and triangular flow, as well as elliptic () and quadrupole () flow observables. We find…
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Taxonomy
TopicsHigh-Energy Particle Collisions Research · Nuclear physics research studies · Statistical Mechanics and Entropy
