Towards AI-assisted Neutrino Flavor Theory Design
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| author | Baretz, Jason Benjamin Fieg, Max Ganesh, Vijay Ghosh, Aishik Knapp-Perez, V. Rudolph, Jake Whiteson, Daniel |
| author_facet | Baretz, Jason Benjamin Fieg, Max Ganesh, Vijay Ghosh, Aishik Knapp-Perez, V. Rudolph, Jake Whiteson, Daniel |
| contents | Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model's construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign field representations, and extract predictions for comparison with experimental data. We develop an Autonomous Model Builder (AMBer), a framework in which a reinforcement learning agent interacts with a streamlined physics software pipeline to search these spaces efficiently. AMBer selects symmetry groups, particle content, and group representation assignments to construct viable models while minimizing the number of free parameters introduced. We validate our approach in well-studied regions of theory space and extend the exploration to a novel, previously unexamined symmetry group. While demonstrated in the context of neutrino flavor theories, this approach of reinforcement learning with physics software feedback may be extended to other theoretical model-building problems in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards AI-assisted Neutrino Flavor Theory Design Baretz, Jason Benjamin Fieg, Max Ganesh, Vijay Ghosh, Aishik Knapp-Perez, V. Rudolph, Jake Whiteson, Daniel High Energy Physics - Phenomenology Machine Learning Computational Physics Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model's construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign field representations, and extract predictions for comparison with experimental data. We develop an Autonomous Model Builder (AMBer), a framework in which a reinforcement learning agent interacts with a streamlined physics software pipeline to search these spaces efficiently. AMBer selects symmetry groups, particle content, and group representation assignments to construct viable models while minimizing the number of free parameters introduced. We validate our approach in well-studied regions of theory space and extend the exploration to a novel, previously unexamined symmetry group. While demonstrated in the context of neutrino flavor theories, this approach of reinforcement learning with physics software feedback may be extended to other theoretical model-building problems in the future. |
| title | Towards AI-assisted Neutrino Flavor Theory Design |
| topic | High Energy Physics - Phenomenology Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2506.08080 |