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*: corresponding author
Pre-published on:
January 09, 2023
Published on:
April 06, 2023
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.
DOI: https://doi.org/10.22323/1.430.0036
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