Speaker
Andrea Palermo
(University of Florence and INFN florence)
Description
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.
Primary authors
Andrea Palermo
(University of Florence and INFN florence)
Lucio Anderlini
(Universita e INFN, Firenze (IT))
Maria Paola Lombardo
(INFN)
Co-authors
Andrey Kotov
Anton Trunin
(Samara University)
