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arXiv · 2208.03832

Sampling QCD field configurations with gauge-equivariant flow models

Ryan Abbott·Michael S. Albergo·Aleksandar Botev·Denis Boyda·Kyle Cranmer·Daniel C. Hackett·Gurtej Kanwar·Alexander G. D. G. Matthews·Sébastien Racanière·Ali Razavi·Danilo J. Rezende·Fernando Romero-López·Phiala E. Shanahan·Julian M. Urban
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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.

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Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban. 2022-08-07. Sampling QCD field configurations with gauge-equivariant flow models. https://arxiv.org/abs/2208.03832

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