Nuclear equation of state at finite $\mu_B$ using deep learning assisted quasi-parton model

TL;DR

This paper introduces a deep learning assisted quasi-parton model to accurately reconstruct the QCD nuclear equation of state at finite baryon chemical potential, aligning well with lattice QCD results and informing heavy-ion collision simulations.

Contribution

The study develops a novel deep learning framework that predicts the QCD EoS at finite $$ and transport coefficients, advancing the modeling of nuclear matter under extreme conditions.

Findings

EoS from the model agrees with lattice QCD results.

The minimum shear viscosity to entropy ratio is about 175 MeV.

The model predicts decreasing $/s$ with increasing chemical potential.

Abstract

To accurately determine the nuclear equation of state (EoS) at finite baryon chemical potential ($\mu_B$) remains a challenging yet essential goal in the study of QCD matter under extreme conditions. In this study, we develop a deep learning assisted quasi-parton model, which utilizes three deep neural networks, to reconstruct the QCD EoS at zero $\mu_B$ and predict the EoS and transport coefficient $\eta/s$ at finite $\mu_B$. The EoS derived from our quasi-parton model shows excellent agreement with lattice QCD results obtained using Taylor expansion techniques. The minimum value of $\eta/s$ is found to be approximately 175 MeV and decreases with increasing chemical potential within the confidence interval. This model not only provides a robust framework for understanding the properties of the QCD EoS at finite $\mu_B$ but also offers critical input for relativistic hydrodynamic simulations of nuclear matter produced in heavy-ion collisions by the RHIC beam energy scan program.