Sampling QCD field configurations with gauge-equivariant flow models
Phiala Elisabeth Shanahan, Ryan Benjamin Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, K. Cranmer, Daniel A. Hackett, Gurtej Kanwar, Alexander Matthews, Sébastien Racanière, Ali Shervin Razavi, Danilo Jimenez Rezende, Fernando Romero-López, Julian M. Urban · Proceedings of The 39th International Symposium on Lattice Field Theory — PoS(LATTICE2022) · 2023
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.