Abi, B;
Acciarri, R;
Acero, MA;
Adamov, G;
Adams, D;
Adinolfi, M;
Ahmad, Z;
... Zwaska, R; + view all
(2020)
Neutrino interaction classification with a convolutional neural network in the DUNE far detector.
Physical Review D
, 102
(9)
, Article 092003. 10.1103/PhysRevD.102.092003.
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Abstract
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure C P -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 C P -violating effects.
| Type: | Article |
|---|---|
| Title: | Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1103/PhysRevD.102.092003 |
| Publisher version: | https://doi.org/10.1103/PhysRevD.102.092003 |
| Language: | English |
| Additional information: | Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. |
| Keywords: | Neutrino interactions, Neutrino oscillations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10116763 |
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