Home > Research > Publications & Outputs > Neutrino interaction classification with a conv...

Associated organisational unit

Electronic data

  • 2006.15052v1

    Submitted manuscript, 2.65 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution to journalJournal articlepeer-review

Published
Article number092003
<mark>Journal publication date</mark>9/11/2020
<mark>Journal</mark>Physical Review D
Volume102
Number of pages20
Publication StatusPublished
<mark>Original language</mark>English

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.