<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

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

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Preprint 12 Oct 2022Embargo end date: 08 Dec 2022 Switzerland, United Kingdom, Korea (Republic of), United Kingdom, United States, India, Netherlands, United Kingdom, China (People's Republic of), France, France, Netherlands, Netherlands, United Kingdom, France, France, Spain, China (People's Republic of), United States, Italy, United Kingdom, France, Netherlands, France Publisher:Springer Science and Business Media LLCJournal:The European Physical Journal C, volume 82 (eissn: 1434-6052,

Copyright policy )Funded by:UKRI | Improved Loss Modelling o..., UKRI | SemenRate Canada/UK: Tran..., UKRI | Developing a novel web pl... +4 projects

Authors: DUNE Collaboration; Abud, A. Abed; Abi, B.; Acciarri, R.; Acero, M. A.; Adames, M. R.; Adamov, G.; +193 Authors

DUNE Collaboration; Abud, A. Abed; Abi, B.; Acciarri, R.; Acero, M. A.; Adames, M. R.; 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.; Monsalve, S. Alonso; 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, L. O.; Arroyave, M. A.; Asaadi, J.; Asquith, L.; Aurisano, A.; Aushev, V.; Autiero, D.; Lara, V. Ayala; Ayala-Torres, M.; Azfar, F.; Babicz, M.; Back, A.; Back, H.; Back, J. J.; 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.; Monarca, J. Barranco; Barros, A.; Barros, N.; Barrow, J. L.; Basharina-Freshville, A.; Bashyal, A.; Basque, V.; Batchelor, C.; Chagas, E. Batista das; Battat, J.; Battisti, F.; Bay, F.; Bazetto, M. C. Q.; Alba, J. Bazo; Beacom, J. F.; Bechetoille, E.; Behera, B.; Beigbeder, C.; Bellantoni, L.; Bellettini, G.; Bellini, V.; Beltramello, O.; Benekos, N.; Montiel, C. Benitez; Neves, F. Bento; Berger, J.; Berkman, S.; Bernardini, P.; Berner, R. M.; Bersani, A.; Bertolucci, S.; Betancourt, M.; Rodríguez, A. Betancur; Bevan, A.; Bezawada, Y.; Bezerra, T. S.; Bhardwaj, A.; Bhatnagar, V.; Bhattacharjee, M.; Bhattarai, D.; Bhuller, S.; Bhuyan, B.; Biagi, S.; Bian, J.; Biassoni, M.; Biery, K.; Bilki, B.; Bishai, M.; Bitadze, A.; Blake, A.; Blaszczyk, F.; Blazey, G.; Blucher, E.; Boissevain, J.; Bolognesi, S.; Bolton, T.; Bomben, L.; Bonesini, M.; Bongrand, M.; Bonilla-Diaz, C.; Bonini, F.; Booth, A.; Boran, F.; Bordoni, S.; Borkum, A.; Bostan, N.; Bour, P.; Bourgeois, C.; Boyden, D.; Bracinik, J.; Braga, D.; Brailsford, D.; Branca, A.; Brandt, A.; Bremer, J.; Breton, D.; Brew, C.; Brice, S. J.; Brizzolari, C.; Bromberg, C.; Brooke, J.; Bross, A.; Brunetti, G.; Brunetti, M.; Buchanan, N.; Budd, H.; Butorov, I.; Cagnoli, I.; Cai, T.; Caiulo, D.; Calabrese, R.; Calafiura, P.; Calcutt, J.; Calin, M.; Calvez, S.; Calvo, E.; Caminata, A.; Campanelli, M.; Caratelli, D.; Carber, D.; Carceller, J.; Carini, G.; Carlus, B.; Carneiro, M. F.; Carniti, P.; Terrazas, I. Caro; Carranza, H.; Carroll, T.; Forero, J. F. Castaño; Castillo, A.; Castromonte, C.; Catano-Mur, E.; Cattadori, C.; Cavalier, F.; Cavallaro, G.; Cavanna, F.; Centro, S.; Cerati, G.; Cervelli, A.; Villanueva, A. Cervera; Chalifour, M.; Chappell, A.; Chardonnet, E.; Charitonidis, N.; Chatterjee, A.; Chattopadhyay, S.; Neyra, M. Chavarry; Chen, H.; Chen, M.; Chen, Y.; Chen, Z.; Chen-Wishart, Z.; Cheon, Y.; Cherdack, D.;

doi: 10.1140/epjc/s10052-022-10791-2 , 10.17863/cam.91403 , 10.5451/unibas-ep94011 , 10.17863/cam.89424 , 10.48550/arxiv.2203.17053

arXiv: http://arxiv.org/abs/2203.17053

handle: https://repository.ubn.ru.nl/handle/2066/283446 , 11245.1/07ce1fc5-a0f7-4ffb-be4f-78809017564a , 2434/944911 , 10281/396774 , 10481/77744 , 20.500.12008/39745 , 11577/3507745 , 11379/564200 , 11392/2503049 , 11567/1099113 , 11587/475384 , 11585/907091 , 20.500.11769/621771 , 11381/2930791 , 1983/41430a5c-d5f7-41cd-8b0c-927f5939d71f , 1721.1/145851 , 11571/1498616 , 10044/1/100336

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

- Summary
- Subjects
- Related research
  (6)
- Metrics

Abstract

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.

Countries

Switzerland, United Kingdom, Korea (Republic of), United Kingdom, United States, India, Netherlands, United Kingdom, China (People's Republic of), France, France, Netherlands, Netherlands, United Kingdom, France, France, Spain, China (People's Republic of), United States, Italy, United Kingdom, France, Netherlands, France

Related Organizations

Grenoble Alpes University
France
University of Basel
Switzerland
University of Salento
Italy
The University of Texas System
United States
National Institute for Nuclear Physics
Italy

View all View all

Keywords

Physics - Instrumentation and Detectors, QC770-798, Astrophysics, Michel electrons, Liquid argon time projection chamber detector, Physics, Particles & Fields, High Energy Physics - Experiment, neutrino, High Energy Physics - Experiment (hep-ex), Particle and Plasma Physics, Energy deposits, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], Neutrini, argon liquido, physics.ins-det, track data analysis, Physics, Instrumentation and Detectors (physics.ins-det), Nuclear & Particles Physics, QB460-466, neutrino detector, liquid argon TPC, particle identification, Physical Sciences, 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics, 51 Physical Sciences, performance, 5107 Particle and High Energy Physics, data analysis method, [PHYS.HEXP] Physics [physics]/High Energy Physics - Experiment [hep-ex], neural network, Regular Article - Experimental Physics, Particles & Fields, FOS: Physical sciences, Experimental data, Convolutional neural network, Bioengineering, 530, cascade: electromagnetic, Nuclear and particle physics. Atomic energy. Radioactivity, Nuclear, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], High Energy Physics, numerical calculations, 0206 Quantum Physics, Science & Technology, DUNE, hep-ex, Molecular, Settore IINF-01/A - Elettronica, time projection chamber: liquid argon, Neutrino experiments, Physics and Astronomy, efficiency, [PHYS.PHYS.PHYS-INS-DET] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], 0202 Atomic, Experimental High Energy Physics, 7 Affordable and Clean Energy, 5106 Nuclear and Plasma Physics

Filter by relation

All relations

arrow_drop_down

6 Research products, page 1 of 1

First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform
2020IsAmongTopNSimilarDocuments
Supernova neutrino burst detection with the Deep Underground Neutrino Experiment
2021IsAmongTopNSimilarDocuments
Separation Of Track- And Shower-Like Energy Deposits In Protodune-Sp Using A Convolutional Neural Network
2022IsIdenticalTo
Searching for solar KDAR with DUNE
2021IsAmongTopNSimilarDocuments
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
2020IsAmongTopNSimilarDocuments
Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report
2021IsAmongTopNSimilarDocuments

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	6
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%