Machine learning approaches to the QCD transition
High Energy Physics - Lattice
2022-04-08 v2 Statistical Mechanics
High Energy Physics - Theory
Abstract
We study the high temperature transition in pure 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 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.
Cite
@article{arxiv.2111.05216,
title = {Machine learning approaches to the QCD transition},
author = {Andrea Palermo and Lucio Anderlini and Maria Paola Lombardo and Andrey Kotov and Anton Trunin},
journal= {arXiv preprint arXiv:2111.05216},
year = {2022}
}
Comments
Proceedings of the 38th international symposium on Lattice Field Theory, LATTICE2021