Signal-to-noise improvement through neural network contour deformations for 3D $SU(2)$ lattice gauge theory

Conference · Fri Sep 01 00:00:00 EDT 2023

OSTI ID:2000987

Detmold, William ^[1]; Kanwar, Gurtej ^[2]; Lin, Yin  ^[1]; Shanahan, Phiala E. ^[1]; Wagman, Michael L. ^[3]

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1. MIT, Cambridge, CTP; IAIFI, Cambridge
2. U. Bern, AEC; Bern U.
3. Fermilab

Complex contour deformations of the path integral have been demonstrated to significantly improve the signal-to-noise ratio of observables in previous studies of two-dimensional gauge theories with open boundary conditions. In this work, new developments based on gauge fixing and a neural network definition of the deformation are introduced, which enable an effective application to theories in higher dimensions and with generic boundary conditions. Improvements of the signal-to-noise ratio by up to three orders of magnitude for Wilson loop measurements are shown in $SU(2)$ lattice gauge theory in three spacetime dimensions.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 2000987
• Report Number(s): FERMILAB-CONF-23-485-T; arXiv:2309.00600; oai:inspirehep.net:2692810
• Country of Publication: United States
• Language: English