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arXiv · 2309.00600

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

William Detmold·Gurtej Kanwar·Yin Lin·Phiala E. Shanahan·Michael L. Wagman
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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.

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William Detmold, Gurtej Kanwar, Yin Lin, Phiala E. Shanahan, Michael L. Wagman. 2023-09-01. Signal-to-noise improvement through neural network contour deformations for 3D $SU(2)$ lattice gauge theory. https://arxiv.org/abs/2309.00600

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