English

Exploring gauge-fixing conditions with gradient-based optimization

High Energy Physics - Lattice 2024-10-07 v1 Machine Learning

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.

Keywords

Cite

@article{arxiv.2410.03602,
  title  = {Exploring gauge-fixing conditions with gradient-based optimization},
  author = {William Detmold and Gurtej Kanwar and Yin Lin and Phiala E. Shanahan and Michael L. Wagman},
  journal= {arXiv preprint arXiv:2410.03602},
  year   = {2024}
}

Comments

9 pages, 2 figures; Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024)

R2 v1 2026-06-28T19:08:52.590Z