Neural network interpolators for Wilson loops
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917392007823360 |
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| author | Mayer-Steudte, Julian |
| author_facet | Mayer-Steudte, Julian |
| contents | The extraction of the static quark-antiquark potential from lattice QCD suffers from the poor signal-to-noise ratio of Wilson loops at large Euclidean times. To overcome this, smearing methods or the Coulomb gauge are used to improve the ground-state overlap with respect to the straight Wilson line trial state within the Wilson loop. To find excited states, complicated shapes are introduced to generate specific quantum numbers. Here, we introduce a neural-network parametrization of trial states, constructed with gauge-equivariant layers and optimized with a loss function that favors ground and excited states. In the quenched theory, we automatically obtain the interpolators for the ground and excited states. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07226 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural network interpolators for Wilson loops Mayer-Steudte, Julian High Energy Physics - Lattice The extraction of the static quark-antiquark potential from lattice QCD suffers from the poor signal-to-noise ratio of Wilson loops at large Euclidean times. To overcome this, smearing methods or the Coulomb gauge are used to improve the ground-state overlap with respect to the straight Wilson line trial state within the Wilson loop. To find excited states, complicated shapes are introduced to generate specific quantum numbers. Here, we introduce a neural-network parametrization of trial states, constructed with gauge-equivariant layers and optimized with a loss function that favors ground and excited states. In the quenched theory, we automatically obtain the interpolators for the ground and excited states. |
| title | Neural network interpolators for Wilson loops |
| topic | High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2604.07226 |