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

Abstract

The interactions of quarks and gluons are strong in the non-perturbative regime. 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 degrees of freedom are dressed quarks and gluons called quasi-particles, with temperature-dependent masses. We use deep neural networks 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 at zero net baryon chemical potential using these machine learned quasi-particle masses, and calculate the shear viscosity over the entropy density (η/s) as a function of temperature of the hot QCD matter.