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

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 ${\chi }_{i,j,k}^{B,Q,S}$ at ${\mu }_{B,Q,S}=0$, and its predicted EoS at various chemical potentials agrees well with results from the generalized $T^{\prime }$-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.

Comments: 12 pages, 9 figures