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...
| Published in: | Journal of High Energy Physics |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-12-01
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP12(2023)021 |
| _version_ | 1851076892954722304 |
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| author | Satsuki Nishimura Coh Miyao Hajime Otsuka |
| author_facet | Satsuki Nishimura Coh Miyao Hajime Otsuka |
| author_sort | Satsuki Nishimura |
| collection | DOAJ |
| container_title | Journal of High Energy Physics |
| description | 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 21 models to be consistent with experimentally measured masses and mixing angles of quarks and leptons. In particular, an intrinsic value of normal ordering tends to be larger than that of inverted ordering, and the normal ordering is well fitted with the current experimental data in contrast to the inverted ordering. A specific value of effective mass for the neutrinoless double beta decay and a sizable leptonic CP violation induced by an angular component of flavon field are predicted by autonomous behavior of the agent. Our finding results indicate that the reinforcement learning can be a new method for understanding the flavor structure. |
| format | Article |
| id | doaj-art-7e6c4046f4f14499b3cb4e3c6467fca7 |
| institution | Directory of Open Access Journals |
| issn | 1029-8479 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| spelling | doaj-art-7e6c4046f4f14499b3cb4e3c6467fca72025-08-19T22:33:18ZengSpringerOpenJournal of High Energy Physics1029-84792023-12-0120231214010.1007/JHEP12(2023)021Exploring the flavor structure of quarks and leptons with reinforcement learningSatsuki Nishimura0Coh Miyao1Hajime Otsuka2Department of Physics, Kyushu UniversityDepartment of Physics, Kyushu UniversityDepartment of Physics, Kyushu UniversityAbstract 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 21 models to be consistent with experimentally measured masses and mixing angles of quarks and leptons. In particular, an intrinsic value of normal ordering tends to be larger than that of inverted ordering, and the normal ordering is well fitted with the current experimental data in contrast to the inverted ordering. A specific value of effective mass for the neutrinoless double beta decay and a sizable leptonic CP violation induced by an angular component of flavon field are predicted by autonomous behavior of the agent. Our finding results indicate that the reinforcement learning can be a new method for understanding the flavor structure.https://doi.org/10.1007/JHEP12(2023)021Theories of FlavourFlavour SymmetriesNeutrino Mixing |
| spellingShingle | Satsuki Nishimura Coh Miyao Hajime Otsuka Exploring the flavor structure of quarks and leptons with reinforcement learning Theories of Flavour Flavour Symmetries Neutrino Mixing |
| title | Exploring the flavor structure of quarks and leptons with reinforcement learning |
| title_full | Exploring the flavor structure of quarks and leptons with reinforcement learning |
| title_fullStr | Exploring the flavor structure of quarks and leptons with reinforcement learning |
| title_full_unstemmed | Exploring the flavor structure of quarks and leptons with reinforcement learning |
| title_short | Exploring the flavor structure of quarks and leptons with reinforcement learning |
| title_sort | exploring the flavor structure of quarks and leptons with reinforcement learning |
| topic | Theories of Flavour Flavour Symmetries Neutrino Mixing |
| url | https://doi.org/10.1007/JHEP12(2023)021 |
| work_keys_str_mv | AT satsukinishimura exploringtheflavorstructureofquarksandleptonswithreinforcementlearning AT cohmiyao exploringtheflavorstructureofquarksandleptonswithreinforcementlearning AT hajimeotsuka exploringtheflavorstructureofquarksandleptonswithreinforcementlearning |