Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gauge field configurations in simple lattice field theories. A critical question is whether this success will translate to studies of QCD. This Proceedings presents a status update on advances in this area. In particular, it is illustrated how recently developed algorithmic components may be combined to construct flow-based sampling algorithms for QCD in four dimensions. The prospects and challenges for future use of this approach in at-scale applications are summarized.
@article{arxiv.2208.03832,
title = {Sampling QCD field configurations with gauge-equivariant flow models},
author = {Ryan Abbott and Michael S. Albergo and Aleksandar Botev and Denis Boyda and Kyle Cranmer and Daniel C. Hackett and Gurtej Kanwar and Alexander G. D. G. Matthews and Sébastien Racanière and Ali Razavi and Danilo J. Rezende and Fernando Romero-López and Phiala E. Shanahan and Julian M. Urban},
journal= {arXiv preprint arXiv:2208.03832},
year = {2022}
}
Comments
Submitted as a proceedings to the 39th International Symposium on Lattice Field Theory (Lattice 2022)