Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks

TL;DR

This paper develops a four-dimensional QCD equation of state using a physics-informed neural network and a quasi-particle model, constrained by lattice QCD data, for better understanding of the QCD phase diagram.

Contribution

It introduces a deep-learning-assisted quasi-particle model within a physics-informed neural network framework to accurately model the QCD equation of state at finite temperature and chemical potentials.

Findings

Reproduces lattice QCD cumulants at zero chemical potential.

Predicts the EoS at finite chemical potentials consistent with lattice QCD results.

Baryon-strangeness correlation matches preliminary experimental data.

Abstract

The equation of state (EoS) of strongly interacting matter at finite temperature and chemical potentials (baryon, charge, and strangeness) is a crucial input for hydrodynamic simulations of relativistic heavy-ion collisions. We construct a four-dimensional EoS using a deep-learning-assisted quasi-particle model (DLQPM) within a physics-informed neural network (PINN) framework, in which the masses of light quarks, strange quarks, and gluons are parameterized as functions of temperature and chemical potentials ($T, \mu_B, \mu_Q, \mu_S$). The model is constrained by lattice QCD data at vanishing chemical potentials and provides a thermodynamically consistent extrapolation to finite $\mu_{B,Q,S}$. The DLQPM accurately reproduces the lattice-calculated cumulants $\chi^{B,Q,S}_{i,j,k}$ at $\mu_{B,Q,S}=0$, and its predicted EoS at various chemical potentials agrees well with results from the generalized $T'$-expansion method in lattice QCD. Furthermore, the calculated baryon-strangeness correlation $C_{BS}$ is consistent, within uncertainties, with preliminary STAR data. This work offers a reliable EoS for exploring the QCD phase structure in the beam energy scan region.