TY  - CONF
A1  - Palermo, Andrea
A1  - Anderlini, Lucio
A1  - Lombardo, Maria Paola
A1  - Kotov, Andrey
T1  - Machine learning approaches to the QCD transition
N2  - 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.
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/70597
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-705976
ER  -