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

Abi, B. and Acciarri, R. and Acero, M.A. and Giri, Anjan Kumar et. al. (2020) Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Physical Review D, 102 (9). pp. 1-20. ISSN 2470-0010

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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.

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IITH Creators:

IITH Creators	ORCiD
Giri, Anjan Kumar	UNSPECIFIED
N, Sahu	UNSPECIFIED

Item Type:

Article

Additional Information:

This document was prepared by the DUNE Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported by CNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, Institute of Particle Physics and NSERC, Canada; CERN; MŠMT, Czech Republic; ERDF, H2020-EU and MSCA, European Union; CNRS/IN2P3 and CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; Comunidad de Madrid, Fundación “La Caixa” and MICINN, Spain; State Secretariat for Education, Research and Innovation and SNSF, Switzerland; TÜBİTAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America.

Uncontrolled Keywords:

Neutrino;neural network

Subjects:

Physics

Divisions:

Department of Physics

Depositing User:

. LibTrainee 2021

Date Deposited:

14 Jul 2021 04:52

Last Modified:

14 Nov 2022 06:12