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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.10915 |
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| _version_ | 1866911761825792000 |
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| author | Abbar, Sajad Harada, Akira Nagakura, Hiroki |
| author_facet | Abbar, Sajad Harada, Akira Nagakura, Hiroki |
| contents | 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 ($ν$ELN) crosses zero along some directions. While previous studies have demonstrated the detection of axisymmetric $ν$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 $ν$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 $ν$ELN crossings in these models eluded detection by earlier methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Based Detection of Non-Axisymmetric Fast Neutrino Flavor Instabilities in Core-Collapse Supernovae Abbar, Sajad Harada, Akira Nagakura, Hiroki High Energy Astrophysical Phenomena 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 ($ν$ELN) crosses zero along some directions. While previous studies have demonstrated the detection of axisymmetric $ν$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 $ν$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 $ν$ELN crossings in these models eluded detection by earlier methods. |
| title | Machine Learning-Based Detection of Non-Axisymmetric Fast Neutrino Flavor Instabilities in Core-Collapse Supernovae |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2401.10915 |