Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Fernandez, R. Castillo; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G.H.; Conrad, J.M.; Convery, M.; Crespo-Anadón, J.I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Sanchez, L. Escudero; Esquivel, J.; Fleming, B.T.; Foreman, W.; Furmanski, A.P.; Garvey, G.T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C.-M.; Jiang, L.; Johnson, R.A.; Jones, B.J.P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B.R.; Lockwitz, S.; Lorca, D.; Louis, W.C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Caicedo, D.A. Martinez; Meddage, V.; Miceli, T.; Mills, G.B.; Moon, J.; Mooney, M.; Moore, C.D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S.F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J.L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D.W.; Schukraft, A.; Seligman, W.; Shaevitz, M.H.; Sinclair, J.; Snider, E.L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S.R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A.M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van de Water, R.G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D.A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G.P.; Zennamo, J.; Zhang, C.

Author(s)

Bugel, Leonard G.; Conrad, Janet Marie; Hen, Or; Jones, Benjamin James Poyner; Wongjirad, Taritree

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Abstract

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

Date issued

2017-03

URI

http://hdl.handle.net/1721.1/120970

Department

Massachusetts Institute of Technology. Department of Nuclear Science and Engineering; Massachusetts Institute of Technology. Department of Physics; Massachusetts Institute of Technology. Laboratory for Nuclear Science

Journal

Journal of Instrumentation

Publisher

IOP Publishing

Citation

Acciarri, R. et al. “Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber.” Journal of Instrumentation 12, 3 (March 2017): P03011–P03011 © 2017 IOP Publishing Ltd and Sissa Medialab

Version: Original manuscript

ISSN

1748-0221

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