TY - JOUR
T1 - Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs
T2 - Algorithm description and quantitative evaluation with MicroBooNE simulation
AU - MicroBooNE Collaboration
AU - Abratenko, P.
AU - An, R.
AU - Anthony, J.
AU - Arellano, L.
AU - Asaadi, J.
AU - Ashkenazi, A.
AU - Balasubramanian, S.
AU - Baller, B.
AU - Barnes, C.
AU - Barr, G.
AU - Basque, V.
AU - Bathe-Peters, L.
AU - Benevides Rodrigues, O.
AU - Berkman, S.
AU - Bhanderi, A.
AU - Bhat, A.
AU - Bishai, M.
AU - Blake, A.
AU - Bolton, T.
AU - Book, J. Y.
AU - Camilleri, L.
AU - Caratelli, D.
AU - Caro Terrazas, I.
AU - Castillo Fernandez, R.
AU - Cavanna, F.
AU - Cerati, G.
AU - Chen, Y.
AU - Cianci, D.
AU - Conrad, J. M.
AU - Convery, M.
AU - Cooper-Troendle, L.
AU - Crespo-Anadón, J. I.
AU - Del Tutto, M.
AU - Dennis, S. R.
AU - Detje, P.
AU - Devitt, A.
AU - Diurba, R.
AU - Dorrill, R.
AU - Duffy, K.
AU - Dytman, S.
AU - Eberly, B.
AU - Ereditato, A.
AU - Evans, J. J.
AU - Fine, R.
AU - Fiorentini Aguirre, G. A.
AU - Fitzpatrick, R. S.
AU - Fleming, B. T.
AU - Foppiani, N.
AU - Franco, D.
AU - Soderberg, M.
N1 - Publisher Copyright:
© 2022 IOP Publishing Ltd and Sissa Medialab.
PY - 2022/1
Y1 - 2022/1
N2 - Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current νe interactions. This pattern recognition achieves 80-90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νe (νμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.
AB - Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current νe interactions. This pattern recognition achieves 80-90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νe (νμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.
KW - Analysis and statistical methods
KW - Pattern recognition, cluster finding, calibration and fitting methods
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U2 - 10.1088/1748-0221/17/01/P01037
DO - 10.1088/1748-0221/17/01/P01037
M3 - Article
AN - SCOPUS:85124220868
SN - 1748-0221
VL - 17
JO - Journal of Instrumentation
JF - Journal of Instrumentation
IS - 1
M1 - P01037
ER -