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Title: The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

Abstract

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.

Authors:
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Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Nuclear Physics (NP)
Contributing Org.:
MicroBooNE; MicroBooNE Collaboration
OSTI Identifier:
1418407
Alternate Identifier(s):
OSTI ID: 1398805; OSTI ID: 1424970
Report Number(s):
FERMILAB-PUB-17-306-ND; AIDA-2020-PUB-2017-002; arXiv:1708.03135; BNL-203308-2018-JAAM
Journal ID: ISSN 1434-6044; 82; PII: 5481
Grant/Contract Number:  
SC0012704; AC02-07CH11359; 654168
Resource Type:
Published Article
Journal Name:
European Physical Journal. C, Particles and Fields
Additional Journal Information:
Journal Name: European Physical Journal. C, Particles and Fields Journal Volume: 78 Journal Issue: 1; Journal ID: ISSN 1434-6044
Publisher:
Springer
Country of Publication:
Germany
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; pattern recognition; event reconstruction; neutrino detectors

Citation Formats

Acciarri, R., Adams, C., An, R., Anthony, J., Asaadi, J., Auger, M., Bagby, L., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Bass, M., Bay, F., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Carls, B., Castillo Fernandez, R., Cavanna, F., Chen, H., Church, E., Cianci, D., Cohen, E., Collin, G. H., Conrad, J. M., Convery, M., Crespo-Anadón, J. I., Del Tutto, M., Devitt, D., Dytman, S., Eberly, B., Ereditato, A., Escudero Sanchez, L., Esquivel, J., Fadeeva, A. A., Fleming, B. T., Foreman, W., Furmanski, A. P., Garcia-Gamez, D., 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., Hourlier, A., Huang, E. -C., James, C., Jan de Vries, J., Jen, C. -M., Jiang, L., Johnson, R. A., 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., Martinez Caicedo, D. A., 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., Piasetzky, E., Porzio, D., Pulliam, G., Qian, X., Raaf, J. L., Rafique, A., Rochester, L., Rudolf von Rohr, C., Russell, B., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sinclair, J., Smith, A., 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 Pontseele, W., Van de Water, R. G., Viren, B., Weber, M., Wickremasinghe, D. A., Wolbers, S., Wongjirad, T., Woodruff, K., Yang, T., Yates, L., Zeller, G. P., Zennamo, J., and Zhang, C. The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector. Germany: N. p., 2018. Web. doi:10.1140/epjc/s10052-017-5481-6.
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Acciarri, R., Adams, C., An, R., Anthony, J., Asaadi, J., Auger, M., Bagby, L., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Bass, M., Bay, F., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Carls, B., Castillo Fernandez, R., Cavanna, F., Chen, H., Church, E., Cianci, D., Cohen, E., Collin, G. H., Conrad, J. M., Convery, M., Crespo-Anadón, J. I., Del Tutto, M., Devitt, D., Dytman, S., Eberly, B., Ereditato, A., Escudero Sanchez, L., Esquivel, J., Fadeeva, A. A., Fleming, B. T., Foreman, W., Furmanski, A. P., Garcia-Gamez, D., 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., Hourlier, A., Huang, E. -C., James, C., Jan de Vries, J., Jen, C. -M., Jiang, L., Johnson, R. A., 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., Martinez Caicedo, D. A., 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., Piasetzky, E., Porzio, D., Pulliam, G., Qian, X., Raaf, J. L., Rafique, A., Rochester, L., Rudolf von Rohr, C., Russell, B., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sinclair, J., Smith, A., 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 Pontseele, W., Van de Water, R. G., Viren, B., Weber, M., Wickremasinghe, D. A., Wolbers, S., Wongjirad, T., Woodruff, K., Yang, T., Yates, L., Zeller, G. P., Zennamo, J., & Zhang, C. The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector. Germany. https://doi.org/10.1140/epjc/s10052-017-5481-6
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Acciarri, R., Adams, C., An, R., Anthony, J., Asaadi, J., Auger, M., Bagby, L., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Bass, M., Bay, F., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Carls, B., Castillo Fernandez, R., Cavanna, F., Chen, H., Church, E., Cianci, D., Cohen, E., Collin, G. H., Conrad, J. M., Convery, M., Crespo-Anadón, J. I., Del Tutto, M., Devitt, D., Dytman, S., Eberly, B., Ereditato, A., Escudero Sanchez, L., Esquivel, J., Fadeeva, A. A., Fleming, B. T., Foreman, W., Furmanski, A. P., Garcia-Gamez, D., 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., Hourlier, A., Huang, E. -C., James, C., Jan de Vries, J., Jen, C. -M., Jiang, L., Johnson, R. A., 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., Martinez Caicedo, D. A., 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., Piasetzky, E., Porzio, D., Pulliam, G., Qian, X., Raaf, J. L., Rafique, A., Rochester, L., Rudolf von Rohr, C., Russell, B., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sinclair, J., Smith, A., 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 Pontseele, W., Van de Water, R. G., Viren, B., Weber, M., Wickremasinghe, D. A., Wolbers, S., Wongjirad, T., Woodruff, K., Yang, T., Yates, L., Zeller, G. P., Zennamo, J., and Zhang, C. Mon . "The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector". Germany. https://doi.org/10.1140/epjc/s10052-017-5481-6.
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@article{osti_1418407,
title = {The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector},
author = {Acciarri, R. and Adams, C. and An, R. and Anthony, J. and Asaadi, J. and Auger, M. and Bagby, L. and Balasubramanian, S. and Baller, B. and Barnes, C. and Barr, G. and Bass, M. and Bay, F. and Bishai, M. and Blake, A. and Bolton, T. and Camilleri, L. and Caratelli, D. and Carls, B. and Castillo Fernandez, R. and Cavanna, F. and Chen, H. and Church, E. and Cianci, D. and Cohen, E. 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 Escudero Sanchez, L. and Esquivel, J. and Fadeeva, A. A. and Fleming, B. T. and Foreman, W. and Furmanski, A. P. and Garcia-Gamez, D. 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 Hourlier, A. and Huang, E. -C. and James, C. and Jan de Vries, J. and Jen, C. -M. and Jiang, L. and Johnson, R. A. 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 Martinez Caicedo, D. A. 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 Piasetzky, E. and Porzio, D. and Pulliam, G. and Qian, X. and Raaf, J. L. and Rafique, A. and Rochester, L. and Rudolf von Rohr, C. and Russell, B. and Schmitz, D. W. and Schukraft, A. and Seligman, W. and Shaevitz, M. H. and Sinclair, J. and Smith, A. 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 Pontseele, W. and Van de Water, R. G. and Viren, B. and Weber, M. and Wickremasinghe, D. A. and Wolbers, S. and Wongjirad, T. and Woodruff, K. and Yang, T. and Yates, L. and Zeller, G. P. and Zennamo, J. and Zhang, C.},
abstractNote = {The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.},
doi = {10.1140/epjc/s10052-017-5481-6},
journal = {European Physical Journal. C, Particles and Fields},
number = 1,
volume = 78,
place = {Germany},
year = {2018},
month = {1}
}
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