Title
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
Document Type
Article
Publication Date
11-9-2020
Publication Title
Physical Review D
Volume
102
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.
Issue
9
DOI
10.1103/PhysRevD.102.092003
ISSN
2470-0029
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
B. Abi, R. Acciarri, M. A. Acero, et al.. "Neutrino interaction classification with a convolutional neural network in the DUNE far detector" (2020). Physics Faculty Publications. 22.
https://commons.und.edu/pa-fac/22