PoS - Proceedings of Science
Volume 453 - The 40th International Symposium on Lattice Field Theory (LATTICE2023) - Algorithms and Artificial Intelligence
Signal-to-noise improvement through neural network contour deformations for 3D š¯‘ŗš¯‘¼(2) lattice gauge theory
Y.Ā Lin*, W.Ā Detmold, G.Ā Kanwar, P.Ā Shanahan and M.Ā Wagman
Full text: pdf
Pre-published on: December 28, 2023
Published on: November 06, 2024
Abstract
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.
DOI: https://doi.org/10.22323/1.453.0043
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