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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 - Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE
AU - MicroBooNE Collaboration
AU - collaboration, MicroBooNE
AU - Abratenko, P.
AU - Alrashed, M.
AU - An, R.
AU - Anthony, J.
AU - Asaadi, J.
AU - Ashkenazi, A.
AU - Balasubramanian, S.
AU - Baller, B.
AU - Barr, G.
AU - Basque, V.
AU - Bathe-Peters, L.
AU - Rodrigues, O. Benevides
AU - Berkman, S.
AU - Bhanderi, A.
AU - Bhat, A.
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 - Conrad, J. M.
AU - Convery, M.
AU - Cooper-Troendle, L.
AU - Crespo-Anadon, J. I.
AU - Tutto, M. Del
AU - Dennis, S. R.
AU - Devitt, D.
AU - Diurba, R.
AU - Dorrill, R.
AU - Duffy, K.
AU - Dytman, S.
AU - Eberly, B.
AU - Ereditato, A.
AU - Aguirre, G. A. Fiorentini
AU - Fitzpatrick, R. S.
AU - Fleming, B. T.
AU - Foppiani, N.
AU - Franco, D.
AU - Furmanski, A. P.
AU - Garcia-Gamez, D.
AU - Gardiner, S.
AU - Nowak, J.
AU - Thorpe, C.
N1 - © 2021 American Physical Society
PY - 2021/3/26
Y1 - 2021/3/26
N2 - We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's $\nu_e$-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is $\geq 99\%$. For full neutrino interaction simulations, the time for processing one image is $\approx$ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.
AB - We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's $\nu_e$-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is $\geq 99\%$. For full neutrino interaction simulations, the time for processing one image is $\approx$ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.
KW - physics.ins-det
KW - hep-ex
U2 - 10.1103/PhysRevD.103.052012
DO - 10.1103/PhysRevD.103.052012
M3 - Journal article
VL - 103
JO - Physical Review D
JF - Physical Review D
SN - 1550-7998
IS - 5
M1 - 052012
ER -