Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber

Adams, C.; Alrashed, M.; An, R.; Anthony, J.; Asaadi, J.; Ashkenazi, A.; Auger, M.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Bass, M.; Bay, F.; Bhat, A.; Bhattacharya, K.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Caro Terrazas, I.; Carr, R.; Castillo Fernandez, R.; Cavanna, F.; Cerati, G.; Chen, Y.; Church, E.; Cianci, D.; Cohen, E. O.; Collin, G. H.; Conrad, J. M.; Convery, M.; Cooper-Troendle, L.; Crespo-Anadon, J. I.; Del Tutto, M.; Devitt, D.; Diaz, A.; Duffy, K.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Evans, J. J.; Fadeeva, A. A.; Fitzpatrick, R. S.; Fleming, B. T.; Franco, D.; Furmanski, A. P.; Garcia-Gamez, D.; Genty, V.; Goeldi, D.; Gollapinni, S.; Goodwin, O.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Guzowski, P.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Horton-Smith, G. A.; Hourlier, A.; Huang, E. -C.; James, C.; Jan De Vries, J.; Ji, X.; Jiang, L.; Johnson, R. A.; Joshi, J.; Jostlein, H.; Jwa, Y. -J.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Lepetic, I.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Marcocci, S.; Mariani, C.; Marshall, J.; Martin-Albo, J.; Martinez Caicedo, D. A.; Mastbaum, A.; Meddage, V.; Mettler, T.; Mistry, K.; Mogan, A.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murphy, M.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Pandey, V.; Paolone, V.; Papadopoulou, A.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Piasetzky, E.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Ren, L.; Rochester, L.; Ross-Lonergan, M.; Rudolf Von Rohr, C.; Russell, B.; Scanavini, G.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sharankova, R.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Soldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St John, J.; Strauss, T.; Sutton, K.; Sword-Fehlberg, S.; Szelc, A. M.; Tagg, N.; Tang, W.; Terao, K.; Thomson, M.; Thornton, R. T.; Toups, M.; Tsai, Y. -T.; Tufanli, S.; Usher, T.; Van De Pontseele, W.; Van De Water, R. G.; Viren, B.; Weber, M.; Wei, H.; Wickremasinghe, D. A.; Wierman, K.; Williams, Z.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Yarbrough, G.; Yates, L. E.; Zeller, G. P.; Zennamo, J.; Zhang, C.

doi:10.1103/PhysRevD.99.092001

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 νμ charged-current neutral pion data samples.