Sampling QCD field configurations with gauge-equivariant flow models
Phiala E. Shanahan, Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, K. Cranmer, Daniel Hackett, Gurtej Kanwar, Alexander Matthews, Sébastien Racanière, Ali Razavi, Danilo Jimenez Rezende, Fernando Romero-López, Julian Urban
Abstract
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.