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

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Date
2017-03-01
Author
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, Leslie
Caratelli, D.
Carls, B.
Fernandez, R. C.
Cavanna, F.
Chen, H.
Church, E.
Cianci, D.
Collin, G. H.
Conrad, Janet M.
Convery, M.
Crespo-Anadon, J. I.
Del Tutto, M.
Devitt, D.
Dytman, S.
Eberly, B.
Ereditato, A.
Escudero Sanchez, L.
Esquivel, J.
Fleming, B. T.
Foreman, W.
Furmanski, A. P.
Garvey, G. T.
Genty, V.
Goeldi, D.
Golapinni, 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, Glenn A.
James, C.
de Vries, J. J.
Jen, C. M.
Jiang, L.
Johnson, R. A.
Jones, B. J. P.
Joshi, J.
Jostlein, H.
Kaleko, D.
Karagiorgi, Georgia S.
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, Camillo
Marshall, J.
Caicedo, D. A. M.
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. R.
Russell, B.
Schmitz, D. W.
Schukraft, A.
Seligman, W.
Shaevitz, Marjorie Hansen
Sinclair, J.
Snider, E. L.
Soderberg, M.
Söldner-Rembold, S.
Soleti, S. R.
Spentzouris, P.
Spitz, Joshua
St John, J.
Strauss, T.
Szelc, A. M.
Tagg, N.
Terao, K.
Thomson, M.
Toups, M.
Tsai, Y. T.
Tufanli, S.
Usher, T.
de Water, R. G. V.
Viren, B.
Weber, M.
Weston, J.
Wickremasinghe, D. A.
Wolbers, S.
Wongjirad, T.
Woodruff, K.
Yang, T.
Zeller, Geralyn P.
Zennamo, J.
Zhang, C.
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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.
URI
http://hdl.handle.net/10919/81661
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