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.A., 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), 1-20 [10.1103/PhysRevD.102.092003].

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

Bertolucci S.;Mauri N.;Moggi N.;Pascoli S.;Pasqualini L.;Pozzato M.;Zucchelli S.;
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.
2020
Abi B., Acciarri R., Acero M.A., 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), 1-20 [10.1103/PhysRevD.102.092003].
Abi B.; Acciarri R.; Acero M.A.; Adamov G.; Adams D.; Adinolfi M.; Ahmad Z.; Ahmed J.; Alion T.; Alonso Monsalve S.; Alt C.; Anderson J.; Andreopoulos...espandi
File in questo prodotto:
File Dimensione Formato  
PhysRevD.102.092003.pdf

accesso aperto

Descrizione: Articolo su rivista internazionale
Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
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/11585/790764
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 26
  • ???jsp.display-item.citation.isi??? 22
social impact