A Gaussian Process Generative Model for QCD Equation of State

TL;DR

This paper introduces a Gaussian Process Regression model to generate smooth, constraint-based equations of state for nuclear matter, facilitating future Bayesian inference with experimental data from heavy-ion collisions.

Contribution

It presents a novel Gaussian Process approach that incorporates theoretical constraints to produce flexible, unconstrained equations of state near phase transitions.

Findings

Generated diverse equations of state with varying sound speeds.

Demonstrated dependencies of experimental observables on the equations of state.

Laid groundwork for Bayesian inference using heavy-ion collision data.

Abstract

We develop a generative model for the nuclear matter equation of state at zero net baryon density using the Gaussian Process Regression method. We impose first-principles theoretical constraints from lattice QCD and hadron resonance gas at high- and low-temperature regions, respectively. By allowing the trained Gaussian Process Regression model to vary freely near the phase transition region, we generate random smooth cross-over equations of state with different speeds of sound that do not rely on specific parameterizations. We explore a collection of experimental observable dependencies on the generated equations of state, which paves the groundwork for future Bayesian inference studies to use experimental measurements from relativistic heavy-ion collisions to constrain the nuclear matter equation of state.