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@article{Abud:2022:epjc/s10052-022-10791-2,
author = {Abud, AA and Abi, B and Acciarri, R and Acero, MA and Adames, MR and Adamov, G and Adamowski, M and Adams, D and Adinolfi, M and Aduszkiewicz, A and Aguilar, J and Ahmad, Z and Ahmed, J and Aimard, B and Ali-Mohammadzadeh, B and Alion, T and Allison, K and Monsalve, SA and AlRashed, M and Alt, C and Alton, A and Alvarez, R and Amedo, P and Anderson, J and Andreopoulos, C and Andreotti, M and Andrews, M and Andrianala, F and Andringa, S and Anfimov, N and Ankowski, A and Antoniassi, M and Antonova, M and Antoshkin, A and Antusch, S and Aranda-Fernandez, A and Arellano, L and Arnold, LO and Arroyave, MA and Asaadi, J and Asquith, L and Aurisano, A and Aushev, V and Autiero, D and Lara, VA and Ayala-Torres, M and Azfar, F and Babicz, M and Back, A and Back, H and Back, JJ and Backhouse, C and Bagaturia, I and Bagby, L and Balashov, N and Balasubramanian, S and Baldi, P and Baller, B and Bambah, B and Barao, F and Barenboim, G and Barker, G and Barkhouse, W and Barnes, C and Barr, G and Ba},
doi = {epjc/s10052-022-10791-2},
journal = {European Physical Journal C: Particles and Fields},
pages = {1--19},
title = {Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network},
url = {http://dx.doi.org/10.1140/epjc/s10052-022-10791-2},
volume = {82},
year = {2022}
}

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TY  - JOUR
AB  - 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.
AU  - Abud,AA
AU  - Abi,B
AU  - Acciarri,R
AU  - Acero,MA
AU  - Adames,MR
AU  - Adamov,G
AU  - Adamowski,M
AU  - Adams,D
AU  - Adinolfi,M
AU  - Aduszkiewicz,A
AU  - Aguilar,J
AU  - Ahmad,Z
AU  - Ahmed,J
AU  - Aimard,B
AU  - Ali-Mohammadzadeh,B
AU  - Alion,T
AU  - Allison,K
AU  - Monsalve,SA
AU  - AlRashed,M
AU  - Alt,C
AU  - Alton,A
AU  - Alvarez,R
AU  - Amedo,P
AU  - Anderson,J
AU  - Andreopoulos,C
AU  - Andreotti,M
AU  - Andrews,M
AU  - Andrianala,F
AU  - Andringa,S
AU  - Anfimov,N
AU  - Ankowski,A
AU  - Antoniassi,M
AU  - Antonova,M
AU  - Antoshkin,A
AU  - Antusch,S
AU  - Aranda-Fernandez,A
AU  - Arellano,L
AU  - Arnold,LO
AU  - Arroyave,MA
AU  - Asaadi,J
AU  - Asquith,L
AU  - Aurisano,A
AU  - Aushev,V
AU  - Autiero,D
AU  - Lara,VA
AU  - Ayala-Torres,M
AU  - Azfar,F
AU  - Babicz,M
AU  - Back,A
AU  - Back,H
AU  - Back,JJ
AU  - Backhouse,C
AU  - Bagaturia,I
AU  - Bagby,L
AU  - Balashov,N
AU  - Balasubramanian,S
AU  - Baldi,P
AU  - Baller,B
AU  - Bambah,B
AU  - Barao,F
AU  - Barenboim,G
AU  - Barker,G
AU  - Barkhouse,W
AU  - Barnes,C
AU  - Barr,G
AU  - Barranco,Monarca J
AU  - Barros,A
AU  - Barros,N
AU  - Barrow,JL
AU  - Basharina-Freshville,A
AU  - Bashyal,A
AU  - Basque,V
AU  - Batchelor,C
AU  - das,Chagas EB
AU  - Battat,J
AU  - Battisti,F
AU  - Bay,F
AU  - Bazetto,MCQ
AU  - Bazo,Alba J
AU  - Beacom,JF
AU  - Bechetoille,E
AU  - Behera,B
AU  - Beigbeder,C
AU  - Bellantoni,L
AU  - Bellettini,G
AU  - Bellini,V
AU  - Beltramello,O
AU  - Benekos,N
AU  - Benitez,Montiel C
AU  - Neves,FB
AU  - Berger,J
AU  - Berkman,S
AU  - Bernardini,P
AU  - Berner,RM
AU  - Bersani,A
AU  - Bertolucci,S
AU  - Betancourt,M
AU  - Betancur,Rodriguez A
AU  - Bevan,A
AU  - Bezawada,Y
AU  - Bezerra,TS
AU  - Bhardwaj,A
AU  - Bhatnagar,V
AU  - Bhattacharjee,M
AU  - Bhattarai,D
AU  - Bhuller,S
AU  - Bhuyan,B
AU  - Biagi,S
AU  - Bian,J
AU  - Biassoni,M
AU  - Biery,K
AU  - Bilki,B
AU  - Bishai,M
AU  - Bitadze,A
AU  - Blake,A
AU  - Blaszczyk,F
AU  - Blazey,G
AU  - Blucher,E
AU  - Boissevain,J
AU  - Bolognesi,S
AU  - Bolton,T
AU  - Bomben,L
AU  - Bonesini,M
AU  - Bongrand,M
AU  - Bonilla-Diaz,C
AU  - Bonini,F
AU  - Booth,A
AU  - Boran,F
AU  - Bordoni,S
AU  - Borkum,A
AU  - Bostan,N
AU  - Bour,P
AU  - Bourgeois,C
AU  - Boyden,D
AU  - Bracinik,J
AU  - Braga,D
AU  - Brailsford,D
AU  - Branca,A
AU  - Brandt,A
AU  - Bremer,J
AU  - Breton,D
AU  - Brew,C
AU  - Brice,SJ
AU  - Brizzolari,C
AU  - Bromberg,C
AU  - Brooke,J
AU  - Bross,A
AU  - Brunetti,G
AU  - Brunetti,M
AU  - Buchanan,N
AU  - Budd,H
AU  - Butorov,I
AU  - Cagnoli,I
AU  - Cai,T
AU  - Caiulo,D
AU  - Calabrese,R
AU  - Calafiura,P
AU  - Calcutt,J
AU  - Calin,M
AU  - Calvez,S
AU  - Calvo,E
AU  - Caminata,A
AU  - Campanelli,M
AU  - Caratelli,D
AU  - Carber,D
AU  - Carceller,J
AU  - Carini,G
AU  - Carlus,B
AU  - Carneiro,MF
AU  - Carniti,P
AU  - Terrazas,IC
AU  - Carranza,H
AU  - Carroll,T
AU  - Castano
DO  - epjc/s10052-022-10791-2
EP  - 19
PY  - 2022///
SN  - 1124-1861
SP  - 1
TI  - Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
T2  - European Physical Journal C: Particles and Fields
UR  - http://dx.doi.org/10.1140/epjc/s10052-022-10791-2
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000866503200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://link.springer.com/article/10.1140/epjc/s10052-022-10791-2
UR  - http://hdl.handle.net/10044/1/100336
VL  - 82
ER  -

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