Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions. We report new algorithmic developments of gauge-equivariant flow architectures facilitating the generalization to higher-dimensional lattice geometries. Specifically, we discuss masked autoregressive transformations with tractable and unbiased Jacobian determinants, a key ingredient for scalable and asymptotically exact flow-based sampling algorithms. For concreteness, results from a proof-of-principle application to SU(3) lattice gauge theory in four space-time dimensions are reported.
@article{arxiv.2305.02402,
title = {Normalizing flows for lattice gauge theory in arbitrary space-time dimension},
author = {Ryan Abbott and Michael S. Albergo and Aleksandar Botev and Denis Boyda and Kyle Cranmer and Daniel C. Hackett and Gurtej Kanwar and Alexander G. D. G. Matthews and Sébastien Racanière and Ali Razavi and Danilo J. Rezende and Fernando Romero-López and Phiala E. Shanahan and Julian M. Urban},
journal= {arXiv preprint arXiv:2305.02402},
year = {2023}
}