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

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arxiv 2309.00600 v1 pith:KWP7QYA2 submitted 2023-09-01 hep-lat

William Detmold , Gurtej Kanwar , Yin Lin , Phiala E. Shanahan , Michael L. Wagman This is my paper
classification hep-lat
keywords gaugesignal-to-noiseboundaryconditionscontourdeformationsdimensionslattice
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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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Cited by 1 Pith paper

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  1. Wilson loops with neural networks

    hep-lat 2026-02 unverdicted novelty 7.0 of 10

    Neural networks parametrize gauge-invariant interpolators that extract ground-state Wilson loops with improved signal-to-noise ratio compared to traditional methods while preserving gauge invariance.

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