Machine Learning-Based Detection of Non-Axisymmetric Fast Neutrino Flavor Instabilities in Core-Collapse Supernovae
| Autor: | Abbar, Sajad, Harada, Akira, Nagakura, Hiroki |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | In dense neutrino environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), neutrinos can undergo fast flavor conversions (FFC) when their angular distribution of neutrino electron lepton number ($\nu$ELN) crosses zero along some directions. While previous studies have demonstrated the detection of axisymmetric $\nu$ELN crossings in these extreme environments, non-axisymmetric crossings have remained elusive, mostly due to the absence of models for their angular distributions. In this study, we present a pioneering analysis of the detection of non-axisymmetric $\nu$ELN crossings using machine learning (ML) techniques. Our ML models are trained on data from two CCSN simulations, one with rotation and one without, where non-axisymmetric features in neutrino angular distributions play a crucial role. We demonstrate that our ML models achieve detection accuracies exceeding 90\%. This is an important improvement, especially considering that a significant portion of $\nu$ELN crossings in these models eluded detection by earlier methods. Comment: 10 pages, 2 figures |
| Databáze: | arXiv |
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