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arXiv · 2111.05216

Machine learning approaches to the QCD transition

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.

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Andrea Palermo, Lucio Anderlini, Maria Paola Lombardo, Andrey Kotov, Anton Trunin. 2021-11-09. Machine learning approaches to the QCD transition. https://arxiv.org/abs/2111.05216

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