A Gaussian Process Generative Model for QCD Equation of State

2024/10/29 by Gong, Jiaxuan, Hendrik Roch, Chun Shen +2 · 2 citations
Decision Sciences · #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Nuclear Theory (nucl-th) #Scientific Computing and Data Management

paper · pdf · doi:10.48550/arxiv.2410.22160

openalex publication_date 2024/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Characterizing radial flow fluctuations in relativistic heavy-ion collisions at top RHIC and LHC energies
• Bayesian Model Selection and Uncertainty Propagation for Beam Energy Scan Heavy-Ion Collisions

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