1. Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
- Author
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Abed Abud, A, Abi, B, Acciarri, R, Acero, MA, Adames, MR, Adamov, G, Adamowski, M, Adams, D, Adinolfi, M, Aduszkiewicz, A, Aguilar, J, Ahmad, Z, Ahmed, J, Aimard, B, Ali-Mohammadzadeh, B, Alion, T, Allison, K, Alonso Monsalve, S, AlRashed, M, Alt, C, Alton, A, Alvarez, R, Amedo, P, Anderson, J, Andreopoulos, C, Andreotti, M, Andrews, M, Andrianala, F, Andringa, S, Anfimov, N, Ankowski, A, Antoniassi, M, Antonova, M, Antoshkin, A, Antusch, S, Aranda-Fernandez, A, Arellano, L, Arnold, LO, Arroyave, MA, Asaadi, J, Asquith, L, Aurisano, A, Aushev, V, Autiero, D, Ayala Lara, V, Ayala-Torres, M, Azfar, F, Babicz, M, Back, A, Back, H, Back, JJ, Backhouse, C, Bagaturia, I, Bagby, L, Balashov, N, Balasubramanian, S, Baldi, P, Baller, B, Bambah, B, Barao, F, Barenboim, G, Barker, G, Barkhouse, W, Barnes, C, Barr, G, Barranco Monarca, J, Barros, A, Barros, N, Barrow, JL, Basharina-Freshville, A, Bashyal, A, Basque, V, Batchelor, C, Batista Das Chagas, E, Battat, J, Battisti, F, Bay, F, Bazetto, MCQ, Bazo Alba, J, Beacom, JF, Bechetoille, E, Behera, B, Beigbeder, C, Bellantoni, L, Bellettini, G, Bellini, V, Beltramello, O, Benekos, N, Benitez Montiel, C, Bento Neves, F, Berger, J, Berkman, S, Bernardini, P, Berner, RM, Bersani, A, Bertolucci, S, Betancourt, M, Betancur Rodríguez, A, Bevan, A, Bezawada, Y, Yang, T. [0000-0002-3190-9941], Apollo - University of Cambridge Repository, and Yang, T [0000-0002-3190-9941]
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Regular Article - Experimental Physics ,7 Affordable and Clean Energy ,5106 Nuclear and Plasma Physics ,51 Physical Sciences ,5107 Particle and High Energy Physics - Abstract
Funder: SERI and SNSF, Switzerland, Funder: Instituto Nazionale di Fisica Nucleare; doi: http://dx.doi.org/10.13039/501100004007, Funder: CAM, Fundacion “La Caixa”, Junta de Andalucia-FEDER, MICINN, and Xunta de Galicia, Spain, Funder: CERN; doi: http://dx.doi.org/10.13039/100012470, Funder: DOE and NSF, United States of America, Funder: CFI, IPP and NSERC, Canada, Funder: CNRS/IN2P3 and CEA, France, Funder: The Royal Society and UKRI/STFC, United Kingdom, Funder: ERDF, H2020-EU and MSCA, European Union, Funder: NRF, South Korea, Funder: FCT, Portugal, Funder: MSMT, Czech Republic, Funder: CNPq, FAPERJ, FAPEG and FAPESP, Brazil, Funder: TUB.ITAK, Turkey, 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.
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- 2022