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Neutrino interaction classification with a convolutional neural network in the DUNE far detector
Journal article   Open access   Peer reviewed

Neutrino interaction classification with a convolutional neural network in the DUNE far detector

Collaboration, DUNE, C Alt, A Gendotti, Laura Molina Bueno, S Murphy, Balint Radics, Y Rigaut, A Rubbia, D Sgalaberna, Michelle J Dolinski, …
Physical Review, v 102, 092003
09 Nov 2020
DOI: https://doi.org/10.3929/ethz-b-000452639
url
https://doi.org/10.1103/physrevd.102.092003View
Published, Version of Record (VoR)CC BY V4.0,  Open

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.

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