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@article{Abi:2020:10.1103/PhysRevD.102.092003,
author = {Abi, B and Acciarri, R and Acero, MA and Adamov, G and Adams, D and Adinolfi, M and Ahmad, Z and Ahmed, J and Alion, T and Monsalve, SA and Alt, C and Anderson, J and Andreopoulos, C and Andrews, MP and Andrianala, F and Andringa, S and Ankowski, A and Antonova, M and Antusch, S and Aranda-Fernandez, A and Ariga, A and Arnold, LO and Arroyave, MA and Asaadi, J and Aurisano, A and Aushev, V and Autiero, D and Azfar, F and Back, H and Back, JJ and Backhouse, C and Baesso, P and Bagby, L and Bajou, R and Balasubramanian, S and Baldi, P and Bambah, B and Barao, F and Barenboim, G and Barker, GJ and Barkhouse, W and Barnes, C and Barr, G and Monarca, JB and Barros, N and Barrow, JL and Bashyal, A and Basque, V and Bay, F and Alba, JLB and Beacom, JF and Bechetoille, E and Behera, B and Bellantoni, L and Bellettini, G and Bellini, V and Beltramello, O and Belver, D and Benekos, N and Bento, Neves F and Berger, J and Berkman, S and Bernardini, P and Berner, RM and Berns, H and Bertolucci, S a},
doi = {10.1103/PhysRevD.102.092003},
journal = {Physical Review D: Particles, Fields, Gravitation and Cosmology},
pages = {092003  1--092003  20},
title = {Neutrino interaction classification with a convolutional neural network in the DUNE far detector},
url = {http://dx.doi.org/10.1103/PhysRevD.102.092003},
volume = {102},
year = {2020}
}

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TY  - JOUR
AB  - The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.
AU  - Abi,B
AU  - Acciarri,R
AU  - Acero,MA
AU  - Adamov,G
AU  - Adams,D
AU  - Adinolfi,M
AU  - Ahmad,Z
AU  - Ahmed,J
AU  - Alion,T
AU  - Monsalve,SA
AU  - Alt,C
AU  - Anderson,J
AU  - Andreopoulos,C
AU  - Andrews,MP
AU  - Andrianala,F
AU  - Andringa,S
AU  - Ankowski,A
AU  - Antonova,M
AU  - Antusch,S
AU  - Aranda-Fernandez,A
AU  - Ariga,A
AU  - Arnold,LO
AU  - Arroyave,MA
AU  - Asaadi,J
AU  - Aurisano,A
AU  - Aushev,V
AU  - Autiero,D
AU  - Azfar,F
AU  - Back,H
AU  - Back,JJ
AU  - Backhouse,C
AU  - Baesso,P
AU  - Bagby,L
AU  - Bajou,R
AU  - Balasubramanian,S
AU  - Baldi,P
AU  - Bambah,B
AU  - Barao,F
AU  - Barenboim,G
AU  - Barker,GJ
AU  - Barkhouse,W
AU  - Barnes,C
AU  - Barr,G
AU  - Monarca,JB
AU  - Barros,N
AU  - Barrow,JL
AU  - Bashyal,A
AU  - Basque,V
AU  - Bay,F
AU  - Alba,JLB
AU  - Beacom,JF
AU  - Bechetoille,E
AU  - Behera,B
AU  - Bellantoni,L
AU  - Bellettini,G
AU  - Bellini,V
AU  - Beltramello,O
AU  - Belver,D
AU  - Benekos,N
AU  - Bento,Neves F
AU  - Berger,J
AU  - Berkman,S
AU  - Bernardini,P
AU  - Berner,RM
AU  - Berns,H
AU  - Bertolucci,S
AU  - Betancourt,M
AU  - Bezawada,Y
AU  - Bhattacharjee,M
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  - Blanco,Siffert B
AU  - Blaszczyk,FDM
AU  - Blazey,GC
AU  - Blucher,E
AU  - Boissevain,J
AU  - Bolognesi,S
AU  - Bolton,T
AU  - Bonesini,M
AU  - Bongrand,M
AU  - Bonini,F
AU  - Booth,A
AU  - Booth,C
AU  - Bordoni,S
AU  - Borkum,A
AU  - Boschi,T
AU  - Bostan,N
AU  - Bour,P
AU  - Boyd,SB
AU  - Boyden,D
AU  - Bracinik,J
AU  - Braga,D
AU  - Brailsford,D
AU  - Brandt,A
AU  - Bremer,J
AU  - Brew,C
AU  - Brianne,E
AU  - Brice,SJ
AU  - Brizzolari,C
AU  - Bromberg,C
AU  - Brooijmans,G
AU  - Brooke,J
AU  - Bross,A
AU  - Brunetti,G
AU  - Buchanan,N
AU  - Budd,H
AU  - Caiulo,D
AU  - Calafiura,P
AU  - Calcutt,J
AU  - Calin,M
AU  - Calvez,S
AU  - Calvo,E
AU  - Camilleri,L
AU  - Caminata,A
AU  - Campanelli,M
AU  - Caratelli,D
AU  - Carini,G
AU  - Carlus,B
AU  - Carniti,P
AU  - Terrazas,IC
AU  - Carranza,H
AU  - Castillo,A
AU  - Castromonte,C
AU  - Cattadori,C
AU  - Cavalier,F
AU  - Cavanna,F
AU  - Centro,S
AU  - Cerati,G
AU  - Cervelli,A
AU  - Cervera,Villanueva A
AU  - Chalifour,M
AU  - Chang,C
AU  - Chardonnet,E
AU  - Chatterjee,A
AU  - Chattopadhyay,S
AU  - Chaves,J
AU  - Chen,H
AU  - Chen,M
AU  - Chen,Y
AU  - Cherdack,D
AU  - Chi,C
AU  - Childress,S
AU  - Chiriacescu,A
AU  - Cho,K
AU  - Choubey,S
AU  - Christensen,A
AU  - Christian,D
AU  - Christodoulou,G
AU  - Church,E
AU  - Clarke,P
AU  - Coan,TE
AU  - Cocco,AG
AU  - Coelho,JAB
AU  - Conley,E
AU  - Conrad,JM
AU  - Convery,M
AU  - Corwin,L
AU  - Cotte,P
AU  - Cremaldi,L
AU  - Cremonesi,L
AU  - Crespo-Anadon,JI
AU  - Cristaldo,E
AU  - Cross,R
AU  - Cuesta,C
AU  - Cui,Y
AU  - Cussans,D
AU  - Dabrowski,M
AU  - da,Motta H
AU  - Peres,LDS
AU  - David,C
AU  - David
DO  - 10.1103/PhysRevD.102.092003
EP  - 1
PY  - 2020///
SN  - 1550-2368
SP  - 092003
TI  - Neutrino interaction classification with a convolutional neural network in the DUNE far detector
T2  - Physical Review D: Particles, Fields, Gravitation and Cosmology
UR  - http://dx.doi.org/10.1103/PhysRevD.102.092003
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000587596500004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://journals.aps.org/prd/abstract/10.1103/PhysRevD.102.092003
UR  - http://hdl.handle.net/10044/1/85281
VL  - 102
ER  -

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