A machine learning approach to the classification of phase transitions in many flavor QCD

TL;DR

This paper employs normalizing flows, a machine learning technique, to model and classify phase transitions in many-flavor QCD by analyzing the chiral condensate across different simulation parameters.

Contribution

It introduces a novel application of normalizing flows to interpolate and classify phase transition regions in QCD simulations with multiple flavors.

Findings

Successfully modeled the probability distribution of the chiral condensate.

Identified first order and crossover regions in the phase diagram.

Marked the boundary between different phase transition types.

Abstract

Normalizing flows are generative machine learning models which can efficiently approximate probability distributions, using only given samples of a distribution. This architecture is used to interpolate the chiral condensate obtained from QCD simulations with five degenerate quark flavors in the HISQ action. From this a model for the probability distribution of the chiral condensate as function of lattice volume, quark mass and gauge coupling is obtained. Using the model, first order and crossover regions can be classified and the boundary between these regions can be marked by a critical mass. An extension of this model to studies of phase transitions in QCD with variable number of flavors is expected to be possible.