Holographic equation of state matched with hadron gas equation as a tool for the study of the quark-gluon plasma evolution

TL;DR

This paper integrates holographic and hadron gas equations of state using machine learning to improve modeling of quark-gluon plasma evolution in heavy-ion collisions, enabling more accurate simulations.

Contribution

It introduces a novel matching of holographic and hadron gas equations of state with machine learning calibration, enhancing relativistic hydrodynamics simulations.

Findings

Successful calibration with lattice QCD data

Enhanced simulation accuracy of quark-gluon plasma evolution

Implementation of holographic EoS in hydrodynamic codes

Abstract

In this paper, we discuss the matching of the holographic equation of state with the equation of Hadron Resonance Gas for studying the nuclear matter properties within the framework of relativistic heavy-ion collisions. Machine learning methods are applied to the calibration of model's free parameters using the lattice QCD results for the physical values of quark masses. One of the most advanced procedures for matching is used with the function that approximate behavior of both models on particular limit adopted from NEOS equation. Final hadronic spectra are obtained within multi-staged numerical approach of the iEBE-MUSIC and SMASH-vHLLE packages. The code of relativistic hydrodynamics is modified by implementing a tabulated holographic equation of state, enabling simulations of quark-gluon plasma evolution with dynamically generated initial conditions via the 3D Monte Carlo Glauber Model and SMASH. Hybrid iSS+UrQMD and Hadron Sampler+SMASH approaches are utilized at the freeze-out stage.