2024/01/12 by Sajad Abbar, Akira Harada, Abbar, Sajad +3 · 2 citations
Physics and Astronomy · #Astrophysics and Cosmic Phenomena #FOS: Physical sciences #High Energy Astrophysical Phenomena (astro-ph.HE) #Neutrino Physics Research #Particle physics theoretical and experimental studies
paper · pdf · doi:10.48550/arxiv.2401.10915
openalex publication_date 2024/01/12 · openalex created_date 2024/01/24 · openalex updated_date 2026/07/28
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 (νELN) crosses zero along some directions. While previous studies have demonstrated the detection of axisymmetric νELN 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 νELN 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 νELN crossings in these models eluded detection by earlier methods.