Thermodynamic and Transport Properties of Quark-Gluon Plasma at Finite Chemical Potential with a DNN framework

TL;DR

This study employs a deep neural network framework to estimate thermodynamic and transport properties of quark-gluon plasma at finite chemical potential, aligning well with lattice QCD results and offering an efficient modeling approach.

Contribution

The paper introduces a DNN-assisted quasi-particle model to accurately estimate QGP properties at finite baryon chemical potential, extending previous lattice-based methods.

Findings

DNN model reproduces lattice QCD results for the equation of state.

Computed properties include speed of sound, specific heat, viscosity, and conductivity.

Results agree with existing lattice calculations and phenomenological models.

Abstract

The characteristics of a thermal system depend strongly on its response to thermal gradients and the underlying microscopic interactions among constituents. In the present study, we investigate the thermodynamic and transport properties of the quark-gluon plasma (QGP) at finite baryon chemical potential within a deep-learning-assisted quasi-particle model (DLQPM). The temperature ($\mathrm{T}$) and baryon chemical potential ($\mu_B$)-dependent thermal masses of quasi-particles are estimated using neural networks trained to reproduce lattice QCD (lQCD) results for the equation of state, obtained via a Taylor-like expansion around vanishing baryon chemical potential. The trained model acts as an effective emulator, enabling us to estimate the thermodynamic and transport properties at finite $\mu_B$. We compute the speed of sound, specific heat, viscosity, and conductivity of the deconfined medium. Our findings are in good agreement with available lattice calculations and other phenomenological models. The present study demonstrates that a DNN-based approach provides an efficient framework for studying the properties of the QGP at finite baryon density.