University of Sussex
Browse
Jeff Hartnell 6-Published-1.10.20.pdf (1.93 MB)

Supernova neutrino detection in NOvA

Download (1.93 MB)
journal contribution
posted on 2023-06-09, 22:39 authored by M A Acero, P Adamson, G Agam, L Aliaga, Tyler Alion, V Allakhverdian, N Anfimov, A Antoshkin, Lily AsquithLily Asquith, M Baird, Alexander Craig Booth, Jeff HartnellJeff Hartnell, Brett MayesBrett Mayes, D P Méndez, Yibing Zhang
The NOvA long-baseline neutrino experiment uses a pair of large, segmented, liquid-scintillator calorimeters to study neutrino oscillations, using GeV-scale neutrinos from the Fermilab NuMI beam. These detectors are also sensitive to the flux of neutrinos which are emitted during a core-collapse supernova through inverse beta decay interactions on carbon at energies of O(10 MeV). This signature provides a means to study the dominant mode of energy release for a core-collapse supernova occurring in our galaxy. We describe the data-driven software trigger system developed and employed by the NOvA experiment to identify and record neutrino data from nearby galactic supernovae. This technique has been used by NOvA to self-trigger on potential core-collapse supernovae in our galaxy, with an estimated sensitivity reaching out to 10 kpc distance while achieving a detection efficiency of 23% to 49% for supernovae from progenitor stars with masses of 9.6 M? to 27 M?, respectively.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Journal of Cosmology and Astroparticle Physics

ISSN

1475-7516

Publisher

IOP Publishing

Issue

10

Volume

2020

Page range

1-29

Article number

a014

Department affiliated with

  • Physics and Astronomy Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2021-01-07

First Open Access (FOA) Date

2021-10-06

First Compliant Deposit (FCD) Date

2021-01-06

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC