Resumen:
|
[EN] 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 ...[+]
[EN] 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.
[-]
|
Agradecimientos:
|
We thank MacKenzie Warren, Ken'ichiro Nakazato, Tomonori Totani, Adam Burrows, David Vartanyan, and Irene Tamborra for access to the supernova models used in this work and for answering various related questions. This work ...[+]
We thank MacKenzie Warren, Ken'ichiro Nakazato, Tomonori Totani, Adam Burrows, David Vartanyan, and Irene Tamborra for access to the supernova models used in this work and for answering various related questions. This work was supported by MEXT Grant-in-Aid for Scientific Research on Innovative Areas titled "Exploration of Particle Physics and Cosmology with Neutrinos" under grant No. 18H05535, 18H05536, and 18H5537. In addition, participation of individual researchers has been further supported by funds from JSPS, Japan; the European Union's Horizon 2020 Research and Innovation Programme H2020 grant Nos. RISE-GA822070-JENNIFER2 2020 and RISEGA872549-SK2HK; SSTF-BA1402-06, NRF grant Nos. 20090083526, NRF-2015R1A2A1A05001869, NRF-2016R1D1A 1A02936965, NRF-2016R1D1A3B02010606, NRF-2017R1 A2B4012757, and NRF-2018R1A6A1A06024970 funded by the Korean government (MSIP); JSPS-RFBR Grant #20-5250010/20 and the Ministry of Science and Higher Education under contract #075-15-2020-778, Russia; Brazilian Funding agencies, CNPq and CAPES; STFC ST/R00031X/2, ST/T002891/1, ST/V002872/1, Consolidated Grants, UKRI MR/S032843/1 and MR/S034102/1, UK. Software: BONSAI.(Smy 2007), sntools. (Migenda et al. 2021), WCSim, 124. matplotlib.(Hunter 2007), NumPy.(van der Walt et al. 2011), SciPy.(Virtanen et al. 2020)
[-]
|