Numerical analysis of neutrino physics within a high-scale supersymmetry model via machine learning
| Autor: | Chun Liu, Ying-Ke Lei, Zhiqiang Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Physics
Nuclear and High Energy Physics Scale (ratio) business.industry Numerical analysis High Energy Physics::Phenomenology General Physics and Astronomy Astronomy and Astrophysics Supersymmetry Machine learning computer.software_genre Symmetry (physics) CP violation High Energy Physics::Experiment Artificial intelligence Neutrino business computer Phenomenology (particle physics) Lepton |
| Zdroj: | Modern Physics Letters A. 35:2050218 |
| ISSN: | 1793-6632 0217-7323 |
| DOI: | 10.1142/s0217732320502181 |
| Popis: | A machine learning method is applied to analyze lepton mass matrices numerically. The matrices were obtained within a framework of high-scale supersymmetry (SUSY) and a flavor symmetry, which are too complicated to be solved analytically. In this numerical calculation, the heuristic method in machine learning is adopted. Neutrino masses, mixings, and CP violation are obtained. It is found that neutrinos are normally ordered and the favorable effective Majorana mass is about [Formula: see text]. |
| Databáze: | OpenAIRE |
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