Deep-learning quasi-particle masses from QCD equation of state

TL;DR

This paper employs deep neural networks to determine temperature-dependent quasi-particle masses in QCD, accurately reproducing the equation of state and calculating transport properties like shear viscosity.

Contribution

It introduces a novel deep learning approach to solve for quasi-particle masses in QCD, enhancing the modeling of strongly-interacting matter.

Findings

Successfully reproduces QCD equation of state using machine-learned masses

Calculates shear viscosity over entropy density as a function of temperature

Demonstrates deep neural networks can solve complex variational problems in QCD

Abstract

The interactions of quarks and gluons are strong at non-perturbative region. The equation of state (EoS) of a strongly-interacting quantum chromodynamics (QCD) medium can only be studied using the first-principle lattice QCD calculations. However, the complicated QCD EoS can be reproduced using simple statistical formula by treating the medium as a free parton gas whose fundamental degree of freedoms are dressed quarks and gluons called quasi-particles, with temperature-dependent masses. We use deep neural network and auto differentiation to solve this variational problem in which the masses of quasi gluons, up/down and strange quarks are three unknown functions, whose forms are represented by deep neural network. We reproduce the QCD EoS using these machine learned quasi-particle masses, and calculate the shear viscosity over entropy density ($\eta/s$) as a function of temperature of the hot QCD matter.