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High Energy Physics - Lattice

arXiv:2111.05216 (hep-lat)
[Submitted on 9 Nov 2021 (v1), last revised 7 Apr 2022 (this version, v2)]

Title:Machine learning approaches to the QCD transition

Authors:Andrea Palermo, Lucio Anderlini, Maria Paola Lombardo, Andrey Kotov, Anton Trunin
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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.
Comments: Proceedings of the 38th international symposium on Lattice Field Theory, LATTICE2021
Subjects: High Energy Physics - Lattice (hep-lat); Statistical Mechanics (cond-mat.stat-mech); High Energy Physics - Theory (hep-th)
Cite as: arXiv:2111.05216 [hep-lat]
  (or arXiv:2111.05216v2 [hep-lat] for this version)
  https://doi.org/10.48550/arXiv.2111.05216

Submission history

From: Andrea Palermo [view email]
[v1] Tue, 9 Nov 2021 15:55:56 UTC (616 KB)
[v2] Thu, 7 Apr 2022 13:16:12 UTC (562 KB)
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