Abstract

The multi-messenger observation of the next galactic core-collapse supernova will shed light on the different physical processes involved in these energetic explosions. Good timing and pointing capabilities of neutrino detectors would help in the search for an electromagnetic or gravitational-wave counterparts. An approach for the determination of the arrival time delay of the neutrino signal at different experiments using a direct detected neutrino light-curve matching is discussed. A simplified supernova model and detector simulation are used for its application. The arrival time delay and its uncertainty between two neutrino detectors are estimated with chi-square and cross-correlation methods. The direct comparison of the detected light-curves offers the advantage to be model-independent. Millisecond time resolution on the arrival time delay at two different detectors is needed. Using the computed time delay between different combinations of currently operational and future detectors, a triangulation method is used to infer the supernova localisation in the sky. The combination of IceCube, Hyper-Kamiokande, JUNO and KM3NeT/ARCA provides a 90% confidence area of 140±20deg2. These low-latency analysis methods can be implemented in the SNEWS alert system.

Details

Title
Combining neutrino experimental light-curves for pointing to the next galactic core-collapse supernova
Author
Coleiro, A 1   VIAFID ORCID Logo  ; Colomer, Molla M 2   VIAFID ORCID Logo  ; Dornic, D 3   VIAFID ORCID Logo  ; Lincetto, M 3   VIAFID ORCID Logo  ; Kulikovskiy, V 4   VIAFID ORCID Logo 

 Université de Paris, CNRS, Astroparticule et Cosmologie, Paris, France 
 Université de Paris, CNRS, Astroparticule et Cosmologie, Paris, France; IFIC, Instituto de Física Corpuscular (CSIC, Universitat de València), Valencia, Spain (GRID:grid.5338.d) (ISNI:0000 0001 2173 938X) 
 Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France (GRID:grid.470046.1) (ISNI:0000 0004 0452 0652) 
 INFN, Sezione di Genova, Genoa, Italy (GRID:grid.470205.4) 
Publication year
2020
Publication date
Sep 2020
Publisher
Springer Nature B.V.
ISSN
14346044
e-ISSN
14346052
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2473254805
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.