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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.24494 |
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| _version_ | 1866911236789108736 |
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| author | Li, Zeyu Zhu, Guorui He, Wenjie Feng, Bo Chen, Jiaqi Luo, Ming-xing Yang, Gang |
| author_facet | Li, Zeyu Zhu, Guorui He, Wenjie Feng, Bo Chen, Jiaqi Luo, Ming-xing Yang, Gang |
| contents | We employ the Transformer to learn patterns in two-dimensional lattice Yang-Mills theory. Specifically, we represent both Wilson loops and their expectation values as tokenized sequences. Taking the shape of Wilson loops as input, the model successfully predicts expectation values with high accuracy, indicating a meaningful connection between loop geometry and physical results. Our study differs from prior machine learning applications in lattice QCD by emphasizing analytical structures rather than numerical computations. We explore model performance under varying hyperparameters, training data sizes, and sequence lengths. This work serves as a first step toward extending such methods to higher dimensions and inspiring rigorous analytical derivations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24494 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI for Pattern Hunter: Application in Wilson Loop of 2D Lattice Yang-Mills Theory Li, Zeyu Zhu, Guorui He, Wenjie Feng, Bo Chen, Jiaqi Luo, Ming-xing Yang, Gang High Energy Physics - Theory We employ the Transformer to learn patterns in two-dimensional lattice Yang-Mills theory. Specifically, we represent both Wilson loops and their expectation values as tokenized sequences. Taking the shape of Wilson loops as input, the model successfully predicts expectation values with high accuracy, indicating a meaningful connection between loop geometry and physical results. Our study differs from prior machine learning applications in lattice QCD by emphasizing analytical structures rather than numerical computations. We explore model performance under varying hyperparameters, training data sizes, and sequence lengths. This work serves as a first step toward extending such methods to higher dimensions and inspiring rigorous analytical derivations. |
| title | AI for Pattern Hunter: Application in Wilson Loop of 2D Lattice Yang-Mills Theory |
| topic | High Energy Physics - Theory |
| url | https://arxiv.org/abs/2510.24494 |