Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

Abratenko, P.; An, R.; Anthony, J.; Arellano, L.; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Barrow, J.; Basque, V.; Bathe-Peters, L.; Benevides Rodrigues, O.; Berkman, S.; Bhanderi, A.; Bhat, A.; Bishai, M.; Blake, A.; Bolton, T.; Book, J. Y.; Camilleri, L.; Caratelli, D.; Caro Terrazas, I.; Cavanna, F.; Cerati, G.; Chen, Y.; Church, E.; Cianci, D.; Conrad, J. M.; Convery, M.; Cooper-Troendle, L.; Crespo-Anadón, J. I.; Del Tutto, M.; Dennis, S. R.; Detje, P.; Devitt, A.; Diurba, R.; Dorrill, R.; Duffy, K.; Dytman, S.; Eberly, B.; Ereditato, A.; Evans, J. J.; Fine, R.; Fiorentini Aguirre, G. A.; Fitzpatrick, R. S.; Fleming, B. T.; Foppiani, N.; Franco, D.; Furmanski, A. P.; Garcia-Gamez, D.; Gardiner, S.; Ge, G.; Gollapinni, S.; Goodwin, O.; Gramellini, E.; Green, P.; Greenlee, H.; Gu, W.; Guenette, R.; Guzowski, P.; Hagaman, L.; Hen, O.; Hilgenberg, C.; Horton-Smith, G. A.; Hourlier, A.; Itay, R.; James, C.; Ji, X.; Jiang, L.; Jo, J. H.; Johnson, R. A.; Jwa, Y. -J.; Kalra, D.; Kamp, N.; Kaneshige, N.; Karagiorgi, G.; Ketchum, W.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Lepetic, I.; Li, J. -Y.; Li, K.; Li, Y.; Lin, K.; Littlejohn, B. R.; Louis, W. C.; Luo, X.; Manivannan, K.; Mariani, C.; Marsden, D.; Marshall, J.; Martinez Caicedo, D. A.; Mason, K.; Mastbaum, A.; McConkey, N.; Meddage, V.; Mettler, T.; Miller, K.; Mills, J.; Mistry, K.; Mogan, A.; Mohayai, T.; Moon, J.; Mooney, M.; Moor, A. F.; Moore, C. D.; Mora Lepin, L.; Mousseau, J.; Mulleriababu, S.; Murphy, M.; Naples, D.; Navrer-Agasson, A.; Nebot-Guinot, M.; Neely, R. K.; Newmark, D. A.; Nowak, J.; Nunes, M.; Palamara, O.; Paolone, V.; Papadopoulou, A.; Papavassiliou, V.; Pate, S. F.; Patel, N.; Paudel, A.; Pavlovic, Z.; Piasetzky, E.; Ponce-Pinto, I. D.; Prince, S.; Qian, X.; Raaf, J. L.; Radeka, V.; Rafique, A.; Reggiani-Guzzo, M.; Ren, L.; Rice, L. C.J.; Rochester, L.; Rodriguez Rondon, J.; Rosenberg, M.; Ross-Lonergan, M.; Scanavini, G.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sharankova, R.; Shi, J.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Spentzouris, P.; Spitz, J.; Stancari, M.; St. John, J.; Strauss, T.; Sutton, K.; Sword-Fehlberg, S.; Szelc, A. M.; Tagg, N.; Tang, W.; Terao, K.; Thorpe, C.; Totani, D.; Toups, M.; Tsai, Y. -T.; Uchida, M. A.; Usher, T.; Van De Pontseele, W.; Viren, B.; Weber, M.; Wei, H.; Williams, Z.; Wolbers, S.; Wongjirad, T.; Wospakrik, M.; Wresilo, K.; Wright, N.; Wu, W.; Yandel, E.; Yang, T.; Yarbrough, G.; Yates, L. E.; Yu, F. J.; Yu, H. W.; Zeller, G. P.; Zennamo, J.; Zhang, C.

doi:10.1088/1748-0221/17/09/p09015

Title: Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

Abstract

In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. Furthermore, this event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition tomore »« less

Authors:: Abratenko, P.; An, R.; Anthony, J.; Arellano, L.; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Barrow, J.; Basque, V.; Bathe-Peters, L.; Benevides Rodrigues, O.; Berkman, S.; Bhanderi, A.; Bhat, A.; Bishai, M.; Blake, A.; Bolton, T. more »« less

Publication Date:: Mon Sep 12 00:00:00 EDT 2022

Research Org.:: Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Org.:: USDOE Office of Science (SC), High Energy Physics (HEP)

Contributing Org.:: The MicroBooNE collaboration

OSTI Identifier:: 1906296

Alternate Identifier(s):: OSTI ID: 1972669

Grant/Contract Number:: SC0023471; AC02-07CH11359; SC0007859

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Instrumentation

Additional Journal Information:: Journal Volume: 17; Journal Issue: 09; Journal ID: ISSN 1748-0221

Publisher:: Institute of Physics (IOP)

Country of Publication:: United States

Language:: English

Subject:: 47 OTHER INSTRUMENTATION; Particle identification methods; Pattern recognition, cluster finding, calibration and fitting methods; Time projection chambers

Citation Formats


                    Abratenko, P., An, R., Anthony, J., Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Barrow, J., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Book, J. Y., Camilleri, L., Caratelli, D., Caro Terrazas, I., Cavanna, F., Cerati, G., Chen, Y., Church, E., Cianci, D., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadón, J. I., Del Tutto, M., Dennis, S. R., Detje, P., Devitt, A., Diurba, R., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Evans, J. J., Fine, R., Fiorentini Aguirre, G. A., Fitzpatrick, R. S., Fleming, B. T., Foppiani, N., Franco, D., Furmanski, A. P., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Goodwin, O., Gramellini, E., Green, P., Greenlee, H., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Hourlier, A., Itay, R., James, C., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. -J., Kalra, D., Kamp, N., Kaneshige, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lepetic, I., Li, J. -Y., Li, K., Li, Y., Lin, K., Littlejohn, B. R., Louis, W. C., Luo, X., Manivannan, K., Mariani, C., Marsden, D., Marshall, J., Martinez Caicedo, D. A., Mason, K., Mastbaum, A., McConkey, N., Meddage, V., Mettler, T., Miller, K., Mills, J., Mistry, K., Mogan, A., Mohayai, T., Moon, J., Mooney, M., Moor, A. F., Moore, C. D., Mora Lepin, L., Mousseau, J., Mulleriababu, S., Murphy, M., Naples, D., Navrer-Agasson, A., Nebot-Guinot, M., Neely, R. K., Newmark, D. A., Nowak, J., Nunes, M., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Patel, N., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I. D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rice, L. C.J., Rochester, L., Rodriguez Rondon, J., Rosenberg, M., Ross-Lonergan, M., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Söldner-Rembold, S., Spentzouris, P., Spitz, J., Stancari, M., St. John, J., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Totani, D., Toups, M., Tsai, Y. -T., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wright, N., Wu, W., Yandel, E., Yang, T., Yarbrough, G., Yates, L. E., Yu, F. J., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C. Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN.  United States: N. p., 2022. 
Web.  doi:10.1088/1748-0221/17/09/p09015.

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                    Abratenko, P., An, R., Anthony, J., Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Barrow, J., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Book, J. Y., Camilleri, L., Caratelli, D., Caro Terrazas, I., Cavanna, F., Cerati, G., Chen, Y., Church, E., Cianci, D., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadón, J. I., Del Tutto, M., Dennis, S. R., Detje, P., Devitt, A., Diurba, R., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Evans, J. J., Fine, R., Fiorentini Aguirre, G. A., Fitzpatrick, R. S., Fleming, B. T., Foppiani, N., Franco, D., Furmanski, A. P., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Goodwin, O., Gramellini, E., Green, P., Greenlee, H., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Hourlier, A., Itay, R., James, C., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. -J., Kalra, D., Kamp, N., Kaneshige, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lepetic, I., Li, J. -Y., Li, K., Li, Y., Lin, K., Littlejohn, B. R., Louis, W. C., Luo, X., Manivannan, K., Mariani, C., Marsden, D., Marshall, J., Martinez Caicedo, D. A., Mason, K., Mastbaum, A., McConkey, N., Meddage, V., Mettler, T., Miller, K., Mills, J., Mistry, K., Mogan, A., Mohayai, T., Moon, J., Mooney, M., Moor, A. F., Moore, C. D., Mora Lepin, L., Mousseau, J., Mulleriababu, S., Murphy, M., Naples, D., Navrer-Agasson, A., Nebot-Guinot, M., Neely, R. K., Newmark, D. A., Nowak, J., Nunes, M., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Patel, N., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I. D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rice, L. C.J., Rochester, L., Rodriguez Rondon, J., Rosenberg, M., Ross-Lonergan, M., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Söldner-Rembold, S., Spentzouris, P., Spitz, J., Stancari, M., St. John, J., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Totani, D., Toups, M., Tsai, Y. -T., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wright, N., Wu, W., Yandel, E., Yang, T., Yarbrough, G., Yates, L. E., Yu, F. J., Yu, H. W., Zeller, G. P., Zennamo, J., & Zhang, C. Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN.  United States.  https://doi.org/10.1088/1748-0221/17/09/p09015

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                    Abratenko, P., An, R., Anthony, J., Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Barrow, J., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Book, J. Y., Camilleri, L., Caratelli, D., Caro Terrazas, I., Cavanna, F., Cerati, G., Chen, Y., Church, E., Cianci, D., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadón, J. I., Del Tutto, M., Dennis, S. R., Detje, P., Devitt, A., Diurba, R., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Evans, J. J., Fine, R., Fiorentini Aguirre, G. A., Fitzpatrick, R. S., Fleming, B. T., Foppiani, N., Franco, D., Furmanski, A. P., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Goodwin, O., Gramellini, E., Green, P., Greenlee, H., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Hourlier, A., Itay, R., James, C., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. -J., Kalra, D., Kamp, N., Kaneshige, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lepetic, I., Li, J. -Y., Li, K., Li, Y., Lin, K., Littlejohn, B. R., Louis, W. C., Luo, X., Manivannan, K., Mariani, C., Marsden, D., Marshall, J., Martinez Caicedo, D. A., Mason, K., Mastbaum, A., McConkey, N., Meddage, V., Mettler, T., Miller, K., Mills, J., Mistry, K., Mogan, A., Mohayai, T., Moon, J., Mooney, M., Moor, A. F., Moore, C. D., Mora Lepin, L., Mousseau, J., Mulleriababu, S., Murphy, M., Naples, D., Navrer-Agasson, A., Nebot-Guinot, M., Neely, R. K., Newmark, D. A., Nowak, J., Nunes, M., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Patel, N., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I. D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rice, L. C.J., Rochester, L., Rodriguez Rondon, J., Rosenberg, M., Ross-Lonergan, M., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Söldner-Rembold, S., Spentzouris, P., Spitz, J., Stancari, M., St. John, J., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Totani, D., Toups, M., Tsai, Y. -T., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wright, N., Wu, W., Yandel, E., Yang, T., Yarbrough, G., Yates, L. E., Yu, F. J., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C. Mon .  
"Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN".  United States.  https://doi.org/10.1088/1748-0221/17/09/p09015.  https://www.osti.gov/servlets/purl/1906296.

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@article{osti_1906296,

  title        = {Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN},

  author       = {Abratenko, P. and An, R. and Anthony, J. and Arellano, L. and Asaadi, J. and Ashkenazi, A. and Balasubramanian, S. and Baller, B. and Barnes, C. and Barr, G. and Barrow, J. and Basque, V. and Bathe-Peters, L. and Benevides Rodrigues, O. and Berkman, S. and Bhanderi, A. and Bhat, A. and Bishai, M. and Blake, A. and Bolton, T. and Book, J. Y. and Camilleri, L. and Caratelli, D. and Caro Terrazas, I. and Cavanna, F. and Cerati, G. and Chen, Y. and Church, E. and Cianci, D. and Conrad, J. M. and Convery, M. and Cooper-Troendle, L. and Crespo-Anadón, J. I. and Del Tutto, M. and Dennis, S. R. and Detje, P. and Devitt, A. and Diurba, R. and Dorrill, R. and Duffy, K. and Dytman, S. and Eberly, B. and Ereditato, A. and Evans, J. J. and Fine, R. and Fiorentini Aguirre, G. A. and Fitzpatrick, R. S. and Fleming, B. T. and Foppiani, N. and Franco, D. and Furmanski, A. P. and Garcia-Gamez, D. and Gardiner, S. and Ge, G. and Gollapinni, S. and Goodwin, O. and Gramellini, E. and Green, P. and Greenlee, H. and Gu, W. and Guenette, R. and Guzowski, P. and Hagaman, L. and Hen, O. and Hilgenberg, C. and Horton-Smith, G. A. and Hourlier, A. and Itay, R. and James, C. and Ji, X. and Jiang, L. and Jo, J. H. and Johnson, R. A. and Jwa, Y. -J. and Kalra, D. and Kamp, N. and Kaneshige, N. and Karagiorgi, G. and Ketchum, W. and Kirby, M. and Kobilarcik, T. and Kreslo, I. and Lepetic, I. and Li, J. -Y. and Li, K. and Li, Y. and Lin, K. and Littlejohn, B. R. and Louis, W. C. and Luo, X. and Manivannan, K. and Mariani, C. and Marsden, D. and Marshall, J. and Martinez Caicedo, D. A. and Mason, K. and Mastbaum, A. and McConkey, N. and Meddage, V. and Mettler, T. and Miller, K. and Mills, J. and Mistry, K. and Mogan, A. and Mohayai, T. and Moon, J. and Mooney, M. and Moor, A. F. and Moore, C. D. and Mora Lepin, L. and Mousseau, J. and Mulleriababu, S. and Murphy, M. and Naples, D. and Navrer-Agasson, A. and Nebot-Guinot, M. and Neely, R. K. and Newmark, D. A. and Nowak, J. and Nunes, M. and Palamara, O. and Paolone, V. and Papadopoulou, A. and Papavassiliou, V. and Pate, S. F. and Patel, N. and Paudel, A. and Pavlovic, Z. and Piasetzky, E. and Ponce-Pinto, I. D. and Prince, S. and Qian, X. and Raaf, J. L. and Radeka, V. and Rafique, A. and Reggiani-Guzzo, M. and Ren, L. and Rice, L. C.J. and Rochester, L. and Rodriguez Rondon, J. and Rosenberg, M. and Ross-Lonergan, M. and Scanavini, G. and Schmitz, D. W. and Schukraft, A. and Seligman, W. and Shaevitz, M. H. and Sharankova, R. and Shi, J. and Sinclair, J. and Smith, A. and Snider, E. L. and Soderberg, M. and Söldner-Rembold, S. and Spentzouris, P. and Spitz, J. and Stancari, M. and St. John, J. and Strauss, T. and Sutton, K. and Sword-Fehlberg, S. and Szelc, A. M. and Tagg, N. and Tang, W. and Terao, K. and Thorpe, C. and Totani, D. and Toups, M. and Tsai, Y. -T. and Uchida, M. A. and Usher, T. and Van De Pontseele, W. and Viren, B. and Weber, M. and Wei, H. and Williams, Z. and Wolbers, S. and Wongjirad, T. and Wospakrik, M. and Wresilo, K. and Wright, N. and Wu, W. and Yandel, E. and Yang, T. and Yarbrough, G. and Yates, L. E. and Yu, F. J. and Yu, H. W. and Zeller, G. P. and Zennamo, J. and Zhang, C.},

  abstractNote = {In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. Furthermore, this event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.},

  doi          = {10.1088/1748-0221/17/09/p09015},

  journal      = {Journal of Instrumentation},

  number       = 09,

  volume       = 17,

  place        = {United States},

  year         = {Mon Sep 12 00:00:00 EDT 2022},

  month        = {Mon Sep 12 00:00:00 EDT 2022}

}

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Works referenced in this record:

Design and construction of the MicroBooNE detector
journal, February 2017

Acciarri, R.; Adams, C.; An, R.
Journal of Instrumentation, Vol. 12, Issue 02
DOI: 10.1088/1748-0221/12/02/P02017

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
journal, March 2017

Acciarri, R.; Adams, C.; An, R.
Journal of Instrumentation, Vol. 12, Issue 03
DOI: 10.1088/1748-0221/12/03/P03011

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
journal, May 2019

Adams, C.; Alrashed, M.; An, R.
Physical Review D, Vol. 99, Issue 9
DOI: 10.1103/PhysRevD.99.092001

Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
journal, May 2021

Abratenko, P.; Alrashed, M.; An, R.
Physical Review D, Vol. 103, Issue 9
DOI: 10.1103/PhysRevD.103.092003

Ionization electron signal processing in single phase LArTPCs. Part I. Algorithm Description and quantitative evaluation with MicroBooNE simulation
journal, July 2018

Adams, C.; An, R.; Anthony, J.
Journal of Instrumentation, Vol. 13, Issue 07
DOI: 10.1088/1748-0221/13/07/P07006

Ionization electron signal processing in single phase LArTPCs. Part II. Data/simulation comparison and performance in MicroBooNE
journal, July 2018

Adams, C.; An, R.; Anthony, J.
Journal of Instrumentation, Vol. 13, Issue 07
DOI: 10.1088/1748-0221/13/07/P07007

Cosmic Ray Background Removal With Deep Neural Networks in SBND
journal, August 2021

Acciarri, R.; Adams, C.; Andreopoulos, C.
Frontiers in Artificial Intelligence, Vol. 4
DOI: 10.3389/frai.2021.649917

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
journal, January 2018

Acciarri, R.; Adams, C.; An, R.
The European Physical Journal C, Vol. 78, Issue 1
DOI: 10.1140/epjc/s10052-017-5481-6

The Pandora software development kit for pattern recognition
journal, September 2015

Marshall, J. S.; Thomson, M. A.
The European Physical Journal C, Vol. 75, Issue 9
DOI: 10.1140/epjc/s10052-015-3659-3

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector
journal, June 2021

Abratenko, P.; Alrashed, M.; An, R.
Physical Review Applied, Vol. 15, Issue 6
DOI: 10.1103/PhysRevApplied.15.064071

Neutrino event selection in the MicroBooNE liquid argon time projection chamber using Wire-Cell 3D imaging, clustering, and charge-light matching
journal, June 2021

Abratenko, P.; Alrashed, M.; An, R.
Journal of Instrumentation, Vol. 16, Issue 06
DOI: 10.1088/1748-0221/16/06/P06043

Design and construction of the MicroBooNE Cosmic Ray Tagger system
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Adams, C.; Alrashed, M.; An, R.
Journal of Instrumentation, Vol. 14, Issue 04
DOI: 10.1088/1748-0221/14/04/P04004

Deep Residual Learning for Image Recognition
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He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI: 10.1109/CVPR.2016.90

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC
journal, August 2017

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The GENIE Neutrino Monte Carlo Generator: Physics and User Manual
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Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3
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Unexplained Excess of Electronlike Events from a 1-GeV Neutrino Beam
journal, March 2009

Aguilar-Arevalo, A. A.; Anderson, C. E.; Bazarko, A. O.
Physical Review Letters, Vol. 102, Issue 10
DOI: 10.1103/PhysRevLett.102.101802

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Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

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In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrinomore »« less
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Title: Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

Abstract

Citation Formats

Design and construction of the MicroBooNE detector journal, February 2017

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber journal, March 2017

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber journal, May 2019

Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber journal, May 2021

Ionization electron signal processing in single phase LArTPCs. Part I. Algorithm Description and quantitative evaluation with MicroBooNE simulation journal, July 2018

Ionization electron signal processing in single phase LArTPCs. Part II. Data/simulation comparison and performance in MicroBooNE journal, July 2018

Cosmic Ray Background Removal With Deep Neural Networks in SBND journal, August 2021

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector journal, January 2018

The Pandora software development kit for pattern recognition journal, September 2015

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector journal, June 2021

Neutrino event selection in the MicroBooNE liquid argon time projection chamber using Wire-Cell 3D imaging, clustering, and charge-light matching journal, June 2021

Design and construction of the MicroBooNE Cosmic Ray Tagger system journal, April 2019

Deep Residual Learning for Image Recognition conference, June 2016

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC journal, August 2017

The GENIE Neutrino Monte Carlo Generator: Physics and User Manual report, October 2015

Geant4—a simulation toolkit journal, July 2003

Unexplained Excess of Electronlike Events from a 1-GeV Neutrino Beam journal, March 2009

Design and construction of the MicroBooNE detector
journal, February 2017

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
journal, March 2017

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
journal, May 2019

Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
journal, May 2021

Ionization electron signal processing in single phase LArTPCs. Part I. Algorithm Description and quantitative evaluation with MicroBooNE simulation
journal, July 2018

Ionization electron signal processing in single phase LArTPCs. Part II. Data/simulation comparison and performance in MicroBooNE
journal, July 2018

Cosmic Ray Background Removal With Deep Neural Networks in SBND
journal, August 2021

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
journal, January 2018

The Pandora software development kit for pattern recognition
journal, September 2015

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector
journal, June 2021

Neutrino event selection in the MicroBooNE liquid argon time projection chamber using Wire-Cell 3D imaging, clustering, and charge-light matching
journal, June 2021

Design and construction of the MicroBooNE Cosmic Ray Tagger system
journal, April 2019

Deep Residual Learning for Image Recognition
conference, June 2016

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC
journal, August 2017

The GENIE Neutrino Monte Carlo Generator: Physics and User Manual
report, October 2015

Geant4—a simulation toolkit
journal, July 2003

Unexplained Excess of Electronlike Events from a 1-GeV Neutrino Beam
journal, March 2009