Efficient Learning of Lattice Gauge Theories with Fermions
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914216722563072 |
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| author | Shukla, Shreya Yamauchi, Yukari Lokhov, Andrey Y. Lawrence, Scott Jayakumar, Abhijith |
| author_facet | Shukla, Shreya Yamauchi, Yukari Lokhov, Andrey Y. Lawrence, Scott Jayakumar, Abhijith |
| contents | We introduce a learning method for recovering action parameters in lattice field theories. Our method is based on the minimization of a convex loss function constructed using the Schwinger-Dyson relations. We show that score matching, a popular learning method, is a special case of our construction of an infinite family of valid loss functions. Importantly, our general Schwinger-Dyson-based construction applies to gauge theories and models with Grassmann-valued fields used to represent dynamical fermions. In particular, we extend our method to realistic lattice field theories including quantum chromodynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19891 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Learning of Lattice Gauge Theories with Fermions Shukla, Shreya Yamauchi, Yukari Lokhov, Andrey Y. Lawrence, Scott Jayakumar, Abhijith High Energy Physics - Lattice Machine Learning Quantum Physics We introduce a learning method for recovering action parameters in lattice field theories. Our method is based on the minimization of a convex loss function constructed using the Schwinger-Dyson relations. We show that score matching, a popular learning method, is a special case of our construction of an infinite family of valid loss functions. Importantly, our general Schwinger-Dyson-based construction applies to gauge theories and models with Grassmann-valued fields used to represent dynamical fermions. In particular, we extend our method to realistic lattice field theories including quantum chromodynamics. |
| title | Efficient Learning of Lattice Gauge Theories with Fermions |
| topic | High Energy Physics - Lattice Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2512.19891 |