Author ORCID Identifier

Bonavera, Laura: 0000-0001-8039-3876
Ospina, Nataly: 0000-0002-8404-1808
Cafagna, Francesco: 0000-0002-7450-4784
Nakano, Yuuki: 0000-0003-1572-3888
Kurjata, Robert: 0000-0001-8547-910X
Batkiewicz-Kwasniak, Marcela: 0000-0002-0376-9363
Kuze, Masahiro: 0000-0001-8858-8440
Dealtry, Thomas James: 0000-0003-2256-9444
Lamoureux, Mathieu: 0000-0002-8860-5826
O'Keeffe, Helen Mary: 0000-0002-4593-3598
Wilson, Jeanne: 0000-0002-6647-1193
Popov, Boris A.: 0000-0001-5416-9301
Jenkins, Sam: 0000-0002-0982-8141
Moon, Chang-Seong: 0000-0001-8229-7829
Katori, Teppei: 0000-0002-9429-9482
Lawe, Matthew David: 0000-0003-2219-5972
Kormos, Laura Lee: 0000-0002-0955-1672
Doyle, Tristan: 0000-0003-0196-0559
Migenda, Jost: 0000-0002-5350-8049

Document Type

Article

Publication Date

2021

Abstract

Core-collapse supernovae are among the most magnificent events in the observable universe. They produce many of the chemical elements necessary for life to exist and their remnants-neutron stars and black holes-are interesting astrophysical objects in their own right. However, despite millennia of observations and almost a century of astrophysical study, the explosion mechanism of core-collapse supernovae is not yet well understood. Hyper-Kamiokande is a next-generation neutrino detector that will be able to observe the neutrino flux from the next galactic core-collapse supernova in unprecedented detail. We focus on the first 500 ms of the neutrino burst, corresponding to the accretion phase, and use a newly-developed, high-precision supernova event generator to simulate Hyper-Kamiokande's response to five different supernova models. We show that Hyper-Kamiokande will be able to distinguish between these models with high accuracy for a supernova at a distance of up to 100 kpc. Once the next galactic supernova happens, this ability will be a powerful tool for guiding simulations toward a precise reproduction of the explosion mechanism observed in nature.

Publication Source (Journal or Book title)

Astrophysical Journal

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