QCD equation of state at finite μB using deep learning assisted quasi-parton model

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 temperature of smallest $\eta /s$ 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.