Machine learning approaches to the QCD transition

Understand

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.