Sampling QCD field configurations with gauge-equivariant flow models
Publisher DOI
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.
Date Issued
2022-01-09
Publication Type
Conference Item
Subject(s)
Language(s)
en
Author(s)
Abbott, Ryan | |
Albergo, Michael S. | |
Botev, Aleksandar | |
Boyda, Denis | |
Cranmer, Kyle | |
Hackett, Daniel C. | |
Matthews, Alexander G. D. G. | |
Racanière, Sébastien | |
Razavi, Ali | |
Rezende, Danilo | |
Romero-López, Fernando | |
Shanahan, Phiala | |
Urban, Julian M. |
Publisher
Sissa Medialab
Title of Event
Access(Rights)
open.access