Neutrino interaction classification with a convolutional neural network in the DUNE far detector
Name
PhysRevD.102.092003.pdf
Description
Published version
Size
3.76 MB
Format
Adobe PDF
Checksum (MD5)
b2b48ae754877aad80010d431397dcdc
Author(s)
Conrad, Janet
Date Issued
2020
Journal
Physical Review D
Publisher
American Physical Society (APS)
Citation
Conrad, Janet. 2020. "Neutrino interaction classification with a convolutional neural network in the DUNE far detector." Physical Review D, 102 (9).
Version
Final published version
Abstract
© 2020 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/"Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. 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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/PHYSREVD.102.092003
