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Sampling QCD field configurations with gauge-equivariant flow models

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arxiv 2208.03832 v2 pith:V55GTUEW submitted 2022-08-07 hep-lat

Ryan Abbott , Michael S. Albergo , Aleksandar Botev , Denis Boyda , Kyle Cranmer , Daniel C. Hackett , Gurtej Kanwar , Alexander G. D. G. Matthews
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Sébastien Racanière Ali Razavi Danilo J. Rezende Fernando Romero-López Phiala E. Shanahan Julian M. Urban
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classification hep-lat
keywords fieldsamplingchallengesconfigurationscriticaladdressadvancesalgorithmic
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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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Cited by 1 Pith paper

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  1. Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition

    hep-lat 2026-05 unverdicted novelty 7.0 of 10

    Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.

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