A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
Creators: name: MicroBooNE Collaboration
name: Adams, C.
name: Alrashed, M.
name: An, R.
name: Anthony, J.
name: Asaadi, J.
name: Ashkenazi, A.
name: Auger, M.
name: Balasubramanian, S.
name: Baller, B.
name: Barnes, C.
name: Barr, G.
name: Bass, M.
name: Bay, F.
name: Bhat, A.
name: Bhattacharya, K.
name: Bishai, M.
name: Blake, A.
name: Bolton, T.
name: Camilleri, L.
name: Caratelli, D.
name: Terrazas, I. Caro
name: Carr, R.
name: Fernandez, R. Castillo
name: Cavanna, F.
name: Cerati, G.
name: Chen, Y.
name: Church, E.
name: Cianci, D.
name: Cohen, E.
name: Collin, G. H.
name: Conrad, J. M.
name: Convery, M.
name: Cooper-Troendle, L.
name: Crespo-Anadon, J. I.
name: Del Tutto, M.
name: Devitt, D.
name: Diaz, A.
name: Duffy, K.
name: Dytman, S.
name: Eberly, B.
name: Ereditato, A.
name: Sanchez, L. Escudero
name: Esquivel, J.
name: Evans, J. J.
name: Fadeeva, A. A.
name: Fitzpatrick, R. S.
name: Fleming, B. T.
name: Franco, D.
name: Furmanski, A. P.
name: Garcia-Gamez, D.
name: Garvey, G. T.
name: Genty, V.
name: Goeldi, D.
name: Gollapinni, S.
name: Goodwin, O.
name: Gramellini, E.
name: Greenlee, H.
name: Grosso, R.
name: Guenette, R.
name: Guzowski, P.
name: Hackenburg, A.
name: Hamilton, P.
name: Hen, O.
name: Hewes, V
name: Hill, C.
name: Horton-Smith, G. A.
name: Hourlier, A.
name: Huang, E. -C.
name: James, C.
name: de Vries, J. Jan
name: Jiang, L.
name: Johnson, R. A.
name: Joshi, J.
name: Jostlein, H.
name: Jwa, Y. -J.
name: Karagiorgi, G.
name: Ketchum, W.
name: Kirby, B.
name: Kirby, M.
name: Kobilarcik, T.
name: Kreslo, I.
name: Li, Y.
name: Lister, A.
name: Littlejohn, B. R.
name: Lockwitz, S.
name: Lorca, D.
name: Louis, W. C.
name: Luethi, M.
name: Lundberg, B.
name: Luo, X.
name: Marchionni, A.
name: Marcocci, S.
name: Mariani, C.
name: Marshall, J.
name: Martin-Albo, J.
name: Caicedo, D. A. Martinez
name: Mastbaum, A.
name: Meddage, V.
name: Mettler, T.
name: Mills, G. B.
name: Mistry, K.
name: Mogan, A.
name: Moon, J.
name: Mooney, M.
name: Moore, C. D.
name: Mousseau, J.
name: Murphy, M.
name: Murrells, R.
name: Naples, D.
name: Nienaber, P.
name: Nowak, J.
name: Palamara, O.
name: Pandey, V.
name: Paolone, V.
name: Papadopoulou, A.
name: Papavassiliou, V.
name: Pate, S. F.
name: Pavlovic, Z.
name: Piasetzky, E.
name: Porzio, D.
name: Pulliam, G.
name: Qian, X.
name: Raaf, J. L.
name: Rafique, A.
name: Rochester, L.
name: Ross-Lonergan, M.
name: von Rohr, C. Rudolf
name: Russell, B.
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: 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: Thomson, M.
name: Thornton, R. T.
name: Toups, M.
name: Tsai, Y. -T.
name: Tufanli, S.
name: Usher, T.
name: Van De Pontseele, W.
name: Van de Water, R. G.
name: Viren, B.
name: Weber, M.
name: Wei, H.
name: Wickremasinghe, D. A.
name: Wierman, K.
name: Williams, Z.
name: Wolbers, S.
name: Wongjirad, T.
name: Woodruff, K.
name: Yang, T.
name: Yarbrough, G.
name: Yates, L. E.
name: Zeller, G. P.
name: Zennamo, J.
name: Zhang, C.
Publisher: name: arXiv
Year: 2018
Subjects: subject: High Energy Physics - Experiment (hep-ex)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Data Analysis, Statistics and Probability (physics.data-an)
subject: Instrumentation and Detectors (physics.ins-det)
subject: FOS: Physical sciences
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: We have developed a convolutional neural network (CNN) 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 $ν_μ$ charged current neutral pion data samples.