| Record |
Author  |
DUNE Collaboration (Abi, B. et al); Antonova, M.; Barenboim, G.; Cervera-Villanueva, A.; De Romeri, V.; Fernandez Menendez, P.; Garcia-Peris, M.A.; Izmaylov, A.; Martin-Albo, J.; Masud, M.; Mena, O.; Novella, P.; Sorel, M.; Ternes, C.A.; Tortola, M.; Valle, J.W.F. |
| Title |
Neutrino interaction classification with a convolutional neural network in the DUNE far detector |
Type |
Journal Article |
| Year |
2020 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
| Volume |
102 |
Issue |
9 |
Pages |
092003 - 20pp |
| Keywords |
|
| 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. |
| Address |
[Decowski, M. P.; De Jong, P.] Univ Amsterdam, NL-1098 XG Amsterdam, Netherlands, Email: saul.alonso.monsalve@cern.ch; |
| Corporate Author |
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Thesis |
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| Publisher |
Amer Physical Soc |
Place of Publication |
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Editor |
|
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2470-0010 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000587596500004 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4598 |
| Permanent link to this record |
