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.

Abi, B., Acciarri, R., Acero, M., Adamov, G., Adams, D., Adinolfi, M., et al. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. PHYSICAL REVIEW D, 102(9) [10.1103/PhysRevD.102.092003].

Neutrino interaction classification with a convolutional neural network in the DUNE far detector

Biassoni M.;Bonesini M.;Brizzolari C.;Brunetti G.;Carniti P.;Falcone A.;Gotti C.;Pessina G.;Spanu M.;Terranova F.;Torti M.;
2020

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.
Articolo in rivista - Articolo scientifico
neutrino physics
English
9-nov-2020
2020
102
9
092001
open
Abi, B., Acciarri, R., Acero, M., Adamov, G., Adams, D., Adinolfi, M., et al. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. PHYSICAL REVIEW D, 102(9) [10.1103/PhysRevD.102.092003].
File in questo prodotto:
File Dimensione Formato  
10281-296946_VoR.pdf

accesso aperto

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Licenza: Creative Commons
Dimensione 3.85 MB
Formato Adobe PDF
3.85 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/296946
Citazioni
  • Scopus 22
  • ???jsp.display-item.citation.isi??? 20
Social impact