PoS - Proceedings of Science
Volume 396 - The 38th International Symposium on Lattice Field Theory (LATTICE2021) - Poster
Machine learning approaches to the QCD transition
A. Palermo*, L. Anderlini, M.P. Lombardo, A.Y. Kotov and A. Trunin
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Pre-published on: May 16, 2022
Published on:
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.
DOI: https://doi.org/10.22323/1.396.0030
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