Exploring gauge-fixing conditions with gradient-based optimization

• URL: http://arxiv.org/abs/2410.03602v1
• Date: Fri, 4 Oct 2024 17:01:41 GMT
• Title: Exploring gauge-fixing conditions with gradient-based optimization
• Authors: William Detmold, Gurtej Kanwar, Yin Lin, Phiala E. Shanahan, Michael L. Wagman,
• Abstract summary: This work introduces a differentiable parameterization of gauge fixing which is broad enough to cover Landau gauge, Coulomb gauge, and maximal tree gauges. The adjoint state method allows gradient-based optimization to select gauge-fixing schemes that minimize an arbitrary target loss function.
• Score: 1.6186320401954881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lattice gauge fixing is required to compute gauge-variant quantities, for example those used in RI-MOM renormalization schemes or as objects of comparison for model calculations. Recently, gauge-variant quantities have also been found to be more amenable to signal-to-noise optimization using contour deformations. These applications motivate systematic parameterization and exploration of gauge-fixing schemes. This work introduces a differentiable parameterization of gauge fixing which is broad enough to cover Landau gauge, Coulomb gauge, and maximal tree gauges. The adjoint state method allows gradient-based optimization to select gauge-fixing schemes that minimize an arbitrary target loss function.

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