Bayesian inference of nuclear incompressibility from collective flow in mid-central Au+Au collisions at 400--1500 MeV/nucleon
J. M. Wang, X. G. Deng, W. J. Xie, B. A. Li, Y. G. Ma

TL;DR
This paper employs Bayesian inference with Gaussian process emulators to determine the nuclear matter incompressibility from collective flow data in heavy-ion collisions, revealing energy-dependent variations in the nuclear equation of state.
Contribution
It introduces a novel Bayesian approach using Gaussian process emulators to extract nuclear incompressibility from collision data, accounting for momentum dependence effects.
Findings
Inferred K values range from ~189 to 286 MeV depending on energy and model assumptions.
Incompressibility increases with beam energy, indicating a stiffening of the nuclear equation of state.
The method provides precise constraints on nuclear matter properties from experimental flow data.
Abstract
The incompressibility of symmetric nuclear matter (SNM) is determined through a Bayesian analysis of collective flow data from Au + Au collisions at beam energies MeV/nucleon. This analysis utilizes a Gaussian process (GP) emulator applied to the isospin-dependent quantum molecular dynamics (IQMD) model for heavy-ion collisions, both with and without incorporating the momentum dependence of the single-nucleon potentials. Specifically, the inferred incompressibility values are MeV and MeV at MeV/nucleon, respectively, at the 68\% confidence level using rapidity and transverse velocity dependence of proton elliptic flow data, with and without consideration of the momentum dependence. When the transverse momentum dependence of proton-like directed flow data is included, the inferred incompressibility values become…
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Taxonomy
TopicsHigh-Energy Particle Collisions Research · Quantum Chromodynamics and Particle Interactions · Nuclear physics research studies
