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

Abstract

Here, 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. Lastly, we also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

Authors:
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Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Org.:
The MicroBooNE Collaboration; MicroBooNE
OSTI Identifier:
1351727
Alternate Identifier(s):
OSTI ID: 1362054; OSTI ID: 1390716
Report Number(s):
BNL-113707-2017-JA; FERMILAB-PUB-16-538-ND; arXiv:1611.05531
Journal ID: ISSN 1748-0221; R&D Project: PO-022; KA2201020; TRN: US1700609
Grant/Contract Number:  
SC0012704; AC02-07CH11359; AC02-76SF00515
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 12; Journal Issue: 03; Journal ID: ISSN 1748-0221
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 47 OTHER INSTRUMENTATION; MicroBooNE; detector; chamber; convolutional; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY

Citation Formats

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., and Zhang, C. Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber. United States: N. p., 2017. Web. doi:10.1088/1748-0221/12/03/P03011.
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. Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber. United States. doi:10.1088/1748-0221/12/03/P03011.
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., and Zhang, C. Tue . "Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber". United States. doi:10.1088/1748-0221/12/03/P03011. https://www.osti.gov/servlets/purl/1351727.
@article{osti_1351727,
title = {Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber},
author = {Acciarri, R. and Adams, C. and An, R. and Asaadi, J. and Auger, M. and Bagby, L. and Baller, B. and Barr, G. and Bass, M. and Bay, F. and Bishai, M. and Blake, A. and Bolton, T. and Bugel, L. and Camilleri, L. and Caratelli, D. and Carls, B. and Fernandez, R. Castillo and Cavanna, F. and Chen, H. and Church, E. and Cianci, D. and Collin, G. H. and Conrad, J. M. and Convery, M. and Crespo-Anadón, J. I. and Del Tutto, M. and Devitt, D. and Dytman, S. and Eberly, B. and Ereditato, A. and Sanchez, L. Escudero and Esquivel, J. and Fleming, B. T. and Foreman, W. and Furmanski, A. P. and Garvey, G. T. and Genty, V. and Goeldi, D. and Gollapinni, S. and Graf, N. and Gramellini, E. and Greenlee, H. and Grosso, R. and Guenette, R. and Hackenburg, A. and Hamilton, P. and Hen, O. and Hewes, J. and Hill, C. and Ho, J. and Horton-Smith, G. and James, C. and de Vries, J. Jan and Jen, C. -M. and Jiang, L. and Johnson, R. A. and Jones, B. J. P. and Joshi, J. and Jostlein, H. and Kaleko, D. and Karagiorgi, G. and Ketchum, W. and Kirby, B. and Kirby, M. and Kobilarcik, T. and Kreslo, I. and Laube, A. and Li, Y. and Lister, A. and Littlejohn, B. R. and Lockwitz, S. and Lorca, D. and Louis, W. C. and Luethi, M. and Lundberg, B. and Luo, X. and Marchionni, A. and Mariani, C. and Marshall, J. and Caicedo, D. A. Martinez and Meddage, V. and Miceli, T. and Mills, G. B. and Moon, J. and Mooney, M. and Moore, C. D. and Mousseau, J. and Murrells, R. and Naples, D. and Nienaber, P. and Nowak, J. and Palamara, O. and Paolone, V. and Papavassiliou, V. and Pate, S. F. and Pavlovic, Z. and Porzio, D. and Pulliam, G. and Qian, X. and Raaf, J. L. and Rafique, A. and Rochester, L. and von Rohr, C. Rudolf and Russell, B. and Schmitz, D. W. and Schukraft, A. and Seligman, W. and Shaevitz, M. H. and Sinclair, J. and Snider, E. L. and Soderberg, M. and Söldner-Rembold, S. and Soleti, S. R. and Spentzouris, P. and Spitz, J. and St. John, J. and Strauss, T. and Szelc, A. M. and Tagg, N. and Terao, K. and Thomson, M. and Toups, M. and Tsai, Y. -T. and Tufanli, S. and Usher, T. and Van de Water, R. G. and Viren, B. and Weber, M. and Weston, J. and Wickremasinghe, D. A. and Wolbers, S. and Wongjirad, T. and Woodruff, K. and Yang, T. and Zeller, G. P. and Zennamo, J. and Zhang, C.},
abstractNote = {Here, 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. Lastly, we also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.},
doi = {10.1088/1748-0221/12/03/P03011},
journal = {Journal of Instrumentation},
number = 03,
volume = 12,
place = {United States},
year = {Tue Mar 14 00:00:00 EDT 2017},
month = {Tue Mar 14 00:00:00 EDT 2017}
}

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