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

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

Research output: Contribution to journalArticle

30 Scopus citations

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.

Original languageEnglish (US)
Article numberP03011
JournalJournal of Instrumentation
Volume12
Issue number3
DOIs
StatePublished - Mar 14 2017

Keywords

  • Analysis and statistical methods
  • Image filtering
  • Particle identification methods
  • Time projection chambers

ASJC Scopus subject areas

  • Mathematical Physics
  • Instrumentation

Fingerprint Dive into the research topics of 'Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber'. Together they form a unique fingerprint.

  • Cite this

    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. C., Cavanna, F., ... Zhang, C. (2017). Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber. Journal of Instrumentation, 12(3), [P03011]. https://doi.org/10.1088/1748-0221/12/03/P03011