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2022

Sampling QCD field configurations with gauge-equivariant flow models

Abbott, Ryan, Albergo, Michael S., Botev, Aleksandar et al.

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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.

Sampling QCD field configurations with gauge-equivariant flow models · Around

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