3 citations · 3 across the 2 of their papers we have counts for
2 papers
astro-ph.HE2024
Machine Learning-Based Detection of Non-Axisymmetric Fast Neutrino Flavor Instabilities in Core-Collapse Supernovae
Sajad Abbar, Akira Harada, Hiroki Nagakura
In dense neutrino environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), neutrinos can undergo fast flavor conversions (FFC) when their angular distrib…
astro-ph.HE2024★ 3 cited
Correlations and Distinguishability Challenges in Supernova Models: Insights from Future Neutrino Detectors
Maria Manuela Saez, Ermal Rrapaj, Akira Harada +2
This paper explores core-collapse supernovae as crucial targets for neutrino telescopes, addressing uncertainties in their simulation results. We comprehensively analyze eighteen m…