Neutrino interaction classification with a convolutional neural network in the DUNE far detector
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F20%3A00537337" target="_blank" >RIV/68378271:_____/20:00537337 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/20:00344032 RIV/00216208:11320/20:10422129
Result on the web
<a href="http://hdl.handle.net/11104/0315059" target="_blank" >http://hdl.handle.net/11104/0315059</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1103/PhysRevD.102.092003" target="_blank" >10.1103/PhysRevD.102.092003</a>
Alternative languages
Result language
angličtina
Original language name
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
Original language description
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.n
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10303 - Particles and field physics
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Physical Review D
ISSN
2470-0010
e-ISSN
—
Volume of the periodical
102
Issue of the periodical within the volume
9
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
1-20
UT code for WoS article
000587596500004
EID of the result in the Scopus database
2-s2.0-85096669682