Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network-Reference-Cited by-同舟云学术

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

Published:2022-10-12 Issue:10 Volume:82 Page:
ISSN:1434-6052
Container-title:The European Physical Journal C
language:en
Short-container-title:Eur. Phys. J. C

Author:

Abed Abud A.,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.,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 L. O.,Arroyave M. A.,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 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.,Barranco Monarca J.,Barros A.,Barros N.,Barrow J. L.,Basharina-Freshville A.,Bashyal A.,Basque V.,Batchelor C.,Batista das Chagas E.,Battat J.,Battisti F.,Bay F.,Bazetto M. C. Q.,Bazo Alba J.,Beacom J. F.,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 R. M.,Bersani A.,Bertolucci S.,Betancourt M.,Betancur Rodríguez A.,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. 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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.

Funder

SERI and SNSF, Switzerland

Instituto Nazionale di Fisica Nucleare

CAM, Fundacion “La Caixa”, Junta de Andalucia-FEDER, MICINN, and Xunta de Galicia, Spain

CERN

DOE and NSF, United States of America

CFI, IPP and NSERC, Canada

CNRS/IN2P3 and CEA, France

The Royal Society and UKRI/STFC, United Kingdom

ERDF, H2020-EU and MSCA, European Union

NRF, South Korea

FCT, Portugal