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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-12-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP12(2023)021 |