Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
Creators: name: Abed Abud, A
name: Abi, B
name: Acciarri, R
name: Acero, MA
name: Adames, MR
name: Adamov, G
name: Adamowski, M
name: Adams, D
name: Adinolfi, M
name: Aduszkiewicz, A
name: Aguilar, J
name: Ahmad, Z
name: Ahmed, J
name: Aimard, B
name: Ali-Mohammadzadeh, B
name: Alion, T
name: Allison, K
name: Alonso Monsalve, S
name: AlRashed, M
name: Alt, C
name: Alton, A
name: Alvarez, R
name: Amedo, P
name: Anderson, J
name: Andreopoulos, C
name: Andreotti, M
name: Andrews, M
name: Andrianala, F
name: Andringa, S
name: Anfimov, N
name: Ankowski, A
name: Antoniassi, M
name: Antonova, M
name: Antoshkin, A
name: Antusch, S
name: Aranda-Fernandez, A
name: Arellano, L
name: Arnold, LO
name: Arroyave, MA
name: Asaadi, J
name: Asquith, L
name: Aurisano, A
name: Aushev, V
name: Autiero, D
name: Ayala Lara, V
name: Ayala-Torres, M
name: Azfar, F
name: Babicz, M
name: Back, A
name: Back, H
name: Back, JJ
name: Backhouse, C
name: Bagaturia, I
name: Bagby, L
name: Balashov, N
name: Balasubramanian, S
name: Baldi, P
name: Baller, B
name: Bambah, B
name: Barao, F
name: Barenboim, G
name: Barker, G
name: Barkhouse, W
name: Barnes, C
name: Barr, G
name: Barranco Monarca, J
name: Barros, A
name: Barros, N
name: Barrow, JL
name: Basharina-Freshville, A
name: Bashyal, A
name: Basque, V
name: Batchelor, C
name: Batista Das Chagas, E
name: Battat, J
name: Battisti, F
name: Bay, F
name: Bazetto, MCQ
name: Bazo Alba, J
name: Beacom, JF
name: Bechetoille, E
name: Behera, B
name: Beigbeder, C
name: Bellantoni, L
name: Bellettini, G
name: Bellini, V
name: Beltramello, O
name: Benekos, N
name: Benitez Montiel, C
name: Bento Neves, F
name: Berger, J
name: Berkman, S
name: Bernardini, P
name: Berner, RM
name: Bersani, A
name: Bertolucci, S
name: Betancourt, M
name: Betancur Rodríguez, A
name: Bevan, A
name: Bezawada, Y
Publisher: name: Apollo - University of Cambridge Repository
Year: 2022
Subjects: Regular Article - Experimental Physics

description: Funder: SERI and SNSF, Switzerland
description: Funder: Instituto Nazionale di Fisica Nucleare; doi: http://dx.doi.org/10.13039/501100004007
description: Funder: CAM, Fundacion “La Caixa”, Junta de Andalucia-FEDER, MICINN, and Xunta de Galicia, Spain
description: Funder: CERN; doi: http://dx.doi.org/10.13039/100012470
description: Funder: DOE and NSF, United States of America
description: Funder: CFI, IPP and NSERC, Canada
description: Funder: CNRS/IN2P3 and CEA, France
description: Funder: The Royal Society and UKRI/STFC, United Kingdom
description: Funder: ERDF, H2020-EU and MSCA, European Union
description: Funder: NRF, South Korea
description: Funder: FCT, Portugal
description: Funder: MSMT, Czech Republic
description: Funder: CNPq, FAPERJ, FAPEG and FAPESP, Brazil
description: Funder: TUB.ITAK, Turkey
description: AbstractLiquid 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.