Speaker
Denis Boyda
(Far Eastern Federal University)
Description
We investigate power of Machine Learning for Lattice QCD problems. We used three set up. First, we used bare configurations of gauge fields and trained ML model to calculate Polyakov loop: trained at two betas it predicts correct critical value. Second, we used set of Wilson loops for classification of phases: trained in SU(2) ML model gives some signal in SU(3). And third, with spacial distribution of some gauge invariant object we predict phase transition in SU(3) with ML model trained in SU(2).
Authors
Alexander Molochkov
(Far Eastern Federal University)
Denis Boyda
(Far Eastern Federal University)
Dr
Vladimir Goy
Maxim Chernodub
(University of Tours, CNRS)