Machine learning approaches to the QCD transition

2021/11/09 by Palermo, Andrea, Anderlini, Lucio, Lombardo, Maria Paola +2
#FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #High Energy Physics - Theory (hep-th) #Statistical Mechanics (cond-mat.stat-mech)

paper · doi:10.48550/arxiv.2111.05216

Abstract

We study the high temperature transition in pure SU(3) gauge theory and in full QCD with 3D-convolutional neural networks trained as parts of either unsupervised or semi-supervised learning problems. Pure gauge configurations are obtained with the MILC public code and full QCD are from simulations of N_f=2+1+1 Wilson fermions at maximal twist. We discuss the capability of different approaches to identify different phases using as input the configurations of Polyakov loops. To better expose fluctuations, a standardized version of Polyakov loops is also considered.

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