Sampling QCD field configurations with gauge-equivariant flow models
R. Abbott, M. Albergo, A. Botev, D. Boyda, K. Cranmer, D. Hackett, G. Kanwar, A. Matthews, S. Racaniere, A. Razavi, D. Rezende, F. Romero-Lopez, P. Shanahan* and J. Urban
January 09, 2023
April 06, 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.
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