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Towards a complete reconstruction of supernova neutrino spectra in future large liquid-scintillator detectors

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arxiv 1712.06985 v2 pith:P7IP5C5T submitted 2017-12-19 hep-ph astro-ph.HE

classification hep-phastro-ph.HE
keywords neutrinospectrasupernovaliquid-scintillatorcompletedetectorselasticfuture
verification ladder T0 review T1 audit T2 compute T3 formal

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abstract

In this paper, we show how to carry out a relatively more realistic and complete reconstruction of supernova neutrino spectra in the future large liquid-scintillator detectors, by implementing the method of singular value decomposition with a proper regularization. For a core-collapse supernova at a distance of $10~{\rm kpc}$ in the Milky Way, its $\overline{\nu}^{}_e$ spectrum can be precisely determined from the inverse beta-decay process $\overline{\nu}^{}_e + p \to e^+ + n$, for which a $20~{\rm kiloton}$ liquid-scintillator detector with the resolution similar to the Jiangmen Underground Neutrino Observatory (JUNO) may register more than 5000 events. We have to rely predominantly on the elastic neutrino-electron scattering $\nu + e^- \to \nu + e^-$ and the elastic neutrino-proton scattering $\nu + p \to \nu + p$ for the spectra of $\nu^{}_e$ and $\nu^{}_x$, where $\nu$ denotes collectively neutrinos and antineutrinos of all three flavors and $\nu^{}_x$ for $\nu^{}_\mu$ and $\nu^{}_\tau$ as well as their antiparticles. To demonstrate the validity of our approach, we also attempt to reconstruct the neutrino spectra by using the time-integrated neutrino data from the latest numerical simulations of delayed neutrino-driven supernova explosions.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Elastic Neutrino-electron Scattering at the One-loop Level in the Standard Model

    hep-ph 2024-12 conditional novelty 4.0 of 10

    An updated complete one-loop Standard Model calculation of elastic neutrino-electron scattering, validated against prior calculations, is used to predict JUNO and TAO event rates.

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