Correlations and Distinguishability Challenges in Supernova Models: Insights from Future Neutrino Detectors

Maria Manuela Saez, Ermal Rrapaj, Akira Harada, Shigehiro Nagataki, Yong-Zhong Qian.


This paper explores core-collapse supernovae as crucial targets for neutrino telescopes, addressing uncertainties in their simulation results. We comprehensively analyze eighteen modern simulations and discriminate among supernova models using realistic detectors and interactions. A significant correlation between the total neutrino energy and cumulative counts, driven by massive lepton neutrinos and oscillations, is identified, particularly noticeable with the DUNE detector. Bayesian techniques indicate strong potential for model differentiation during a Galactic supernova event, with HK excelling in distinguishing models based on equation of state, progenitor mass, and mixing scheme.

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