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High Energy Physics - Lattice

arXiv:2008.05456 (hep-lat)
[Submitted on 12 Aug 2020 (v1), last revised 18 Sep 2020 (this version, v2)]

Title:Sampling using $SU(N)$ gauge equivariant flows

Authors:Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
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Abstract:We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction. Our key contribution is constructing a class of flows on an $SU(N)$ variable (or on a $U(N)$ variable by a simple alternative) that respect matrix conjugation symmetry. We apply this technique to sample distributions of single $SU(N)$ variables and to construct flow-based samplers for $SU(2)$ and $SU(3)$ lattice gauge theory in two dimensions.
Comments: 24 pages, 19 figures
Subjects: High Energy Physics - Lattice (hep-lat); Machine Learning (cs.LG); Machine Learning (stat.ML)
Report number: MIT-CTP/5228
Cite as: arXiv:2008.05456 [hep-lat]
  (or arXiv:2008.05456v2 [hep-lat] for this version)
  https://doi.org/10.48550/arXiv.2008.05456
Journal reference: Phys. Rev. D 103, 074504 (2021)
Related DOI: https://doi.org/10.1103/PhysRevD.103.074504

Submission history

From: Gurtej Kanwar [view email]
[v1] Wed, 12 Aug 2020 17:43:39 UTC (10,946 KB)
[v2] Fri, 18 Sep 2020 18:39:15 UTC (10,952 KB)
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