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
Publication date: 09/11/2020
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