|   | 
Details
   web
Record
Author (up) 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 Thesis
Publisher Amer Physical Soc Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2470-0010 ISBN Medium
Area Expedition Conference
Notes WOS:000587596500004 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4598
Permanent link to this record