Machine learning approaches to the QCD transition
Andrea Palermo, L. Anderlini, Maria Paola Lombardo, Andrey Yu. Kotov, Anton M. Trunin · Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021) · 2022
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.