Rights statement: © 2019 American Physical Society
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
AU - Adams, C.
AU - Alrashed, M.
AU - An, R.
AU - Anthony, J.
AU - Asaadi, J.
AU - Ashkenazi, A.
AU - Auger, M.
AU - Balasubramanian, S.
AU - Baller, B.
AU - Barnes, C.
AU - Barr, G.
AU - Bass, M.
AU - Bay, F.
AU - Bhat, A.
AU - Bhattacharya, K.
AU - Bishai, M.
AU - Blake, A.
AU - Bolton, T.
AU - Camilleri, L.
AU - Caratelli, D.
AU - Terrazas, I. Caro
AU - Fernandez, R. Castillo
AU - Cavanna, F.
AU - Cerati, G.
AU - Church, E.
AU - Cianci, D.
AU - Cohen, E.
AU - Collin, G. H.
AU - Conrad, J. M.
AU - Convery, M.
AU - Cooper-Troendle, L.
AU - Crespo-Anadon, J. I.
AU - Tutto, M. Del
AU - Devitt, D.
AU - Diaz, A.
AU - Duffy, K.
AU - Dytman, S.
AU - Eberly, B.
AU - Ereditato, A.
AU - Sanchez, L. Escudero
AU - Esquivel, J.
AU - Fadeeva, A. A.
AU - Fitzpatrick, R. S.
AU - Fleming, B. T.
AU - Franco, D.
AU - Furmanski, A. P.
AU - Garcia-Gamez, D.
AU - Garvey, G. T.
AU - Lister, A.
AU - Nowak, J.
AU - MicroBooNE collaboration
N1 - © 2019 American Physical Society
PY - 2019/5/7
Y1 - 2019/5/7
N2 - We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a nu(mu) charged-current neutral pion data samples.
AB - We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a nu(mu) charged-current neutral pion data samples.
KW - physics.ins-det
KW - cs.CV
KW - hep-ex
KW - physics.data-an
U2 - 10.1103/PhysRevD.99.092001
DO - 10.1103/PhysRevD.99.092001
M3 - Journal article
VL - 99
JO - Physical Review D
JF - Physical Review D
SN - 1550-7998
IS - 9
M1 - 092001
ER -