A1 Journal article (refereed)
Neutrino interaction classification with a convolutional neural network in the DUNE far detector (2020)


DUNE Collaboration. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, 102(9), Article 092003. https://doi.org/10.1103/PhysRevD.102.092003


JYU authors or editors


Publication details

All authors or editors: DUNE Collaboration

Journal or series: Physical Review D

ISSN: 2470-0010

eISSN: 2470-0029

Publication year: 2020

Volume: 102

Issue number: 9

Article number: 092003

Publisher: American Physical Society

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1103/PhysRevD.102.092003

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73028

Web address of parallel published publication (pre-print): https://arxiv.org/abs/2006.15052


Abstract

The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.


Keywords: particle physics; neutrinos; neutrino oscillation; classification; machine learning; neural networks (information technology)


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 2


Last updated on 2021-07-07 at 21:37