Exploring gauge-fixing conditions with gradient-based optimization
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912058719600640 |
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| author | Detmold, William Kanwar, Gurtej Lin, Yin Shanahan, Phiala E. Wagman, Michael L. |
| author_facet | Detmold, William Kanwar, Gurtej Lin, Yin Shanahan, Phiala E. Wagman, Michael L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03602 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring gauge-fixing conditions with gradient-based optimization Detmold, William Kanwar, Gurtej Lin, Yin Shanahan, Phiala E. Wagman, Michael L. High Energy Physics - Lattice Machine Learning 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. |
| title | Exploring gauge-fixing conditions with gradient-based optimization |
| topic | High Energy Physics - Lattice Machine Learning |
| url | https://arxiv.org/abs/2410.03602 |