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

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.

Comments: Second version. 9 pages, 5 figures. Added link to ready-to-use EoS tables for hydrodynamic simulations. The reader is encouraged to test the equation of state in their hydrodynamic codes and to reach out if help with implementation is needed