Exploring the flavor structure of quarks and leptons with reinforcement learning
Abstract We propose a method to explore the flavor structure of quarks and leptons with reinforcement learning. As a concrete model, we utilize a basic value-based algorithm for models with U(1) flavor symmetry. By training neural networks on the U(1) charges of quarks and leptons, the agent finds 2...
| 出版年: | Journal of High Energy Physics |
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| 主要な著者: | , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
SpringerOpen
2023-12-01
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| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/JHEP12(2023)021 |