Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Abed, Abud A and Giri, Anjan Kumar and Sahu, Narendra and et al, . (2022) Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. The European Physical Journal C, 82 (10). pp. 1-19. ISSN 1434-6044

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Abstract

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation. © 2022, The Author(s).

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IITH Creators:

IITH Creators	ORCiD
Giri, Anjan Kumar	UNSPECIFIED
Sahu, Narendra	https://orcid.org/0000-0002-9675-0484

Item Type:

Article

Additional Information:

The ProtoDUNE-SP detector was constructed and operated on the CERN Neutrino Platform. We gratefully acknowledge the support of the CERN management, and the CERN EP, BE, TE, EN and IT Departments for NP04/ProtoDUNE-SP. 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, IPP 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; CAM, Fundación “La Caixa”, Junta de Andalucía-FEDER, MICINN, and Xunta de Galicia, Spain; SERI and SNSF, Switzerland; TÜBİTAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.

Uncontrolled Keywords:

Separation, ProtoDUNE-SP, convolutional neural networks

Subjects:

Physics

Divisions:

Department of Physics

Depositing User:

. LibTrainee 2021

Date Deposited:

25 Oct 2022 09:26

Last Modified:

25 Oct 2022 09:26