Deep-learning quasi-particle masses from QCD equation of state
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 () as a function of temperature of the hot QCD matter.
Keywords
Cite
@article{arxiv.2211.07994,
title = {Deep-learning quasi-particle masses from QCD equation of state},
author = {Fu-Peng Li and Hong-Liang Lü and Long-Gang Pang and Guang-You Qin},
journal= {arXiv preprint arXiv:2211.07994},
year = {2023}
}