Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE
Creators: name: MicroBooNE Collaboration
name: Abratenko, P.
name: Alrashed, M.
name: An, R.
name: Anthony, J.
name: Asaadi, J.
name: Ashkenazi, A.
name: Balasubramanian, S.
name: Baller, B.
name: Barnes, C.
name: Barr, G.
name: Basque, V.
name: Bathe-Peters, L.
name: Rodrigues, O. Benevides
name: Berkman, S.
name: Bhanderi, A.
name: Bhat, A.
name: Bishai, M.
name: Blake, A.
name: Bolton, T.
name: Camilleri, L.
name: Caratelli, D.
name: Terrazas, I. Caro
name: Fernandez, R. Castillo
name: Cavanna, F.
name: Cerati, G.
name: Chen, Y.
name: Church, E.
name: Cianci, D.
name: Conrad, J. M.
name: Convery, M.
name: Cooper-Troendle, L.
name: Crespo-Anadon, J. I.
name: Del Tutto, M.
name: Dennis, S. R.
name: Devitt, D.
name: Diurba, R.
name: Dorrill, R.
name: Duffy, K.
name: Dytman, S.
name: Eberly, B.
name: Ereditato, A.
name: Evans, J. J.
name: Aguirre, G. A. Fiorentini
name: Fitzpatrick, R. S.
name: Fleming, B. T.
name: Foppiani, N.
name: Franco, D.
name: Furmanski, A. P.
name: Garcia-Gamez, D.
name: Gardiner, S.
name: Ge, G.
name: Gollapinni, S.
name: Goodwin, O.
name: Gramellini, E.
name: Green, P.
name: Greenlee, H.
name: Gu, W.
name: Guenette, R.
name: Guzowski, P.
name: Hagaman, L.
name: Hall, E.
name: Hamilton, P.
name: Hen, O.
name: Horton-Smith, G. A.
name: Hourlier, A.
name: Itay, R.
name: James, C.
name: de Vries, J. Jan
name: Ji, X.
name: Jiang, L.
name: Jo, J. H.
name: Johnson, R. A.
name: Jwa, Y. J.
name: Kamp, N.
name: Kaneshige, N.
name: Karagiorgi, G.
name: Ketchum, W.
name: Kirby, B.
name: Kirby, M.
name: Kobilarcik, T.
name: Kreslo, I.
name: LaZur, R.
name: Lepetic, I.
name: Li, K.
name: Li, Y.
name: Littlejohn, B. R.
name: Louis, W. C.
name: Luo, X.
name: Marchionni, A.
name: Mariani, C.
name: Marsden, D.
name: Marshall, J.
name: Martin-Albo, J.
name: Caicedo, D. A. Martinez
name: Mason, K.
name: Mastbaum, A.
name: McConkey, N.
name: Meddage, V.
name: Mettler, T.
name: Miller, K.
name: Mills, J.
name: Mistry, K.
name: Mohayai, T.
name: Mogan, A.
name: Moon, J.
name: Mooney, M.
name: Moor, A. F.
name: Moore, C. D.
name: Lepin, L. Mora
name: Mousseau, J.
name: Murphy, M.
name: Naples, D.
name: Navrer-Agasson, A.
name: Neely, R. K.
name: Nienaber, P.
name: Nowak, J.
name: Palamara, O.
name: Paolone, V.
name: Papadopoulou, A.
name: Papavassiliou, V.
name: Pate, S. F.
name: Paudel, A.
name: Pavlovic, Z.
name: Piasetzky, E.
name: Ponce-Pinto, I.
name: Prince, S.
name: Qian, X.
name: Raaf, J. L.
name: Radeka, V.
name: Rafique, A.
name: Reggiani-Guzzo, M.
name: Ren, L.
name: Rochester, L.
name: Rondon, J. Rodriguez
name: Rogers, H. E.
name: Rosenberg, M.
name: Ross-Lonergan, M.
name: Russell, B.
name: Scanavini, G.
name: Schmitz, D. W.
name: Schukraft, A.
name: Seligman, W.
name: Shaevitz, M. H.
name: Sharankova, R.
name: Sinclair, J.
name: Smith, A.
name: Snider, E. L.
name: Soderberg, M.
name: Soldner-Rembold, S.
name: Soleti, S. R.
name: Spentzouris, P.
name: Spitz, J.
name: Stancari, M.
name: John, J. St.
name: Strauss, T.
name: Sutton, K.
name: Sword-Fehlberg, S.
name: Szelc, A. M.
name: Tagg, N.
name: Tang, W.
name: Terao, K.
name: Thorpe, C.
name: Toups, M.
name: Tsai, Y. -T.
name: Uchida, M. A.
name: Usher, T.
name: Van De Pontseele, W.
name: Viren, B.
name: Weber, M.
name: Wei, H.
name: Williams, Z.
name: Wolbers, S.
name: Wongjirad, T.
name: Wospakrik, M.
name: Wu, W.
name: Yandel, E.
name: Yang, T.
name: Yarbrough, G.
name: Yates, L. E.
name: Zeller, G. P.
name: Zennamo, J.
name: Zhang, C.
Publisher: name: arXiv
Year: 2020
Subjects: subject: Instrumentation and Detectors (physics.ins-det)
subject: High Energy Physics - Experiment (hep-ex)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: 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 $ν_e$-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.