Equation of State at High Baryon Densities from a Thermodynamically Informed Neural Network

TL;DR

This paper introduces a neural network-based equation of state for strongly interacting matter, applicable across a wide range of temperatures and densities, useful for modeling relativistic heavy-ion collisions.

Contribution

It develops a thermodynamically consistent neural network model that extrapolates the equation of state into high-density regimes inaccessible to traditional methods.

Findings

Reproduces hadron resonance gas thermodynamics at typical scales.

Aligns with lattice QCD results at low baryon chemical potential.

Provides a high-density extrapolation for heavy-ion collision modeling.

Abstract

We present a four-dimensional equation of state for strongly interacting matter at finite temperature and conserved charge densities, constructed using a deep neural network. It is designed for direct use in hybrid models of relativistic heavy-ion collisions: it reproduces hadron resonance gas thermodynamics at typical particlization scales, is consistent with lattice QCD at low baryon chemical potential, and extrapolates into the high-density region inaccessible to either approach, which is precisely the regime targeted by RHIC BES, FAIR, HADES, and CBM. Thermodynamic consistency throughout the full phase space is enforced via a physics-informed loss function. We demonstrate the developed equation of state by implementing it at zero net strangeness and fixed electric-to-baryon charge ratio within the integrated hydrokinetic model.