Machine learning approaches to the QCD transition

  • 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 Nf=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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Author:Andrea PalermoORCiD, Lucio AnderliniORCiDGND, Maria Paola LombardoORCiDGND, Andrey KotovORCiD
URN:urn:nbn:de:hebis:30:3-705976
DOI:https://doi.org/10.48550/arXiv.2111.05216
ArXiv Id:http://arxiv.org/abs/2111.05216
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2022/04/07
Date of first Publication:2022/04/07
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Contributing Corporation:International Symposium on Lattice Field Theory (38. : 2021 : Online)
Release Date:2023/01/24
Page Number:7
Institutes:Physik / Physik
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Sammlungen:Universit├Ątspublikationen
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International