Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber

Abratenko, P.; Alrashed, M.; An, R.; Anthony, J.; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Basque, V.; Bathe-Peters, L.; Benevides Rodrigues, O.; Berkman, S.; Bhanderi, A.; Bhat, A.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Caro Terrazas, I.; Castillo Fernandez, R.; 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.; Devitt, A.; Diurba, R.; Domine, L.; Dorrill, R.; Duffy, K.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Evans, J. J.; 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.; Hall, E.; Hamilton, P.; Hen, O.; Horton-Smith, G. A.; Hourlier, A.; Huang, E. -C.; Itay, R.; James, C.; Jan de Vries, J.; Ji, X.; Jiang, L.; Jo, J. H.; Johnson, R. A.; Jwa, Y. -J.; Kamp, N.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; LaZur, R.; Lepetic, I.; Li, K.; Li, Y.; Littlejohn, B. R.; Lorca, D.; Louis, W. C.; Luo, X.; Marchionni, A.; Marcocci, S.; Mariani, C.; Marsden, D.; Marshall, J.; Martin-Albo, 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.; Mousseau, J.; Murphy, M.; Naples, D.; Navrer-Agasson, A.; Neely, R. K.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papadopoulou, A.; Papavassiliou, V.; Pate, S. F.; Paudel, A.; Pavlovic, Z.; Piasetzky, E.; Ponce-Pinto, I. D.; Porzio, D.; Prince, S.; Qian, X.; Raaf, J. L.; Radeka, V.; Rafique, A.; Reggiani-Guzzo, M.; Ren, L.; Rochester, L.; Rodriguez Rondon, J.; Rogers, H. E.; Rosenberg, M.; Ross-Lonergan, M.; Russell, B.; Scanavini, G.; Schmitz, D. W.; Schukraft, A.; Shaevitz, M. H.; Sharankova, R.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; Stancari, M.; John, J. St.; Strauss, T.; Sutton, K.; Sword-Fehlberg, S.; Szelc, A. M.; Tagg, N.; Tang, W.; Terao, K.; Thorpe, C.; Toups, M.; Tsai, Y. -T.; Tufanli, S.; Uchida, M. A.; Usher, T.; Van De Pontseele, W.; Viren, B.; Weber, M.; Wei, H.; Williams, Z.; Wolbers, S.; Wongjirad, T.; Wospakrik, M.; Wu, W.; Yang, T.; Yarbrough, G.; Yates, L. E.; Zeller, G. P.; Zennamo, J.; Zhang, C.

doi:10.1103/PhysRevD.103.092003

Title: Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber

Abstract

We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an interaction includes an e^–, γ, μ^–, π^±, and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE [Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017)]. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE’s deep-learning-based ν_e search analysis. In this paper, we present the network’s design, training, and performance on simulation and data from the MicroBooNE detector.

Authors:

Abratenko, P.; Alrashed, M.;

; Anthony, J.; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Basque, V.; Bathe-Peters, L.; Benevides Rodrigues, O.; Berkman, S.; Bhanderi, A.; Bhat, A.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L. more »

Publication Date:: Fri May 14 00:00:00 EDT 2021

Research Org.:: Brookhaven National Laboratory (BNL), Upton, NY (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Harvard Univ., Cambridge, MA (United States); 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), Nuclear Physics (NP)

Contributing Org.:: MicroBooNE; MicroBooNE Collaboration

OSTI Identifier:: 1782986

Alternate Identifier(s):: OSTI ID: 1712763; OSTI ID: 1784016; OSTI ID: 1784473; OSTI ID: 1808694; OSTI ID: 1836524; OSTI ID: 1866748

Report Number(s):: FERMILAB-PUB-20-527-ND; arXiv:2010.08653; BNL-221400-2021-JAAM
Journal ID: ISSN 2470-0010; PRVDAQ; 092003

Grant/Contract Number:: AC02-07CH11359; SC0007881; AC02-76SF00515; SC0020262; SC0012704; SC0007859

Resource Type:: Published Article

Journal Name:: Physical Review D

Additional Journal Information:: Journal Name: Physical Review D Journal Volume: 103 Journal Issue: 9; Journal ID: ISSN 2470-0010

Publisher:: American Physical Society

Country of Publication:: United States

Language:: English

Subject:: 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Neutrinos; Artificial neural networks; Machine learning

Citation Formats


                    Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Caro Terrazas, I., Castillo Fernandez, R., 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., Devitt, A., Diurba, R., Domine, L., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Escudero Sanchez, L., Evans, J. J., 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., Hall, E., Hamilton, P., Hen, O., Horton-Smith, G. A., Hourlier, A., Huang, E. -C., Itay, R., James, C., Jan de Vries, J., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. -J., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, B., Kirby, M., Kobilarcik, T., Kreslo, I., LaZur, R., Lepetic, I., Li, K., Li, Y., Littlejohn, B. R., Lorca, D., Louis, W. C., Luo, X., Marchionni, A., Marcocci, S., Mariani, C., Marsden, D., Marshall, J., Martin-Albo, 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., Mousseau, J., Murphy, M., Naples, D., Navrer-Agasson, A., Neely, R. K., Nienaber, P., Nowak, J., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I. D., Porzio, D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rodriguez Rondon, J., Rogers, H. E., Rosenberg, M., Ross-Lonergan, M., Russell, B., Scanavini, G., Schmitz, D. W., Schukraft, A., Shaevitz, M. H., Sharankova, R., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Söldner-Rembold, S., Soleti, S. R., Spentzouris, P., Spitz, J., Stancari, M., John, J. St., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Toups, M., Tsai, Y. -T., Tufanli, S., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wu, W., Yang, T., Yarbrough, G., Yates, L. E., Zeller, G. P., Zennamo, J., Zhang, C., and MicroBooNE Collaboration. Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber.  United States: N. p., 2021. 
Web.  doi:10.1103/PhysRevD.103.092003.

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                    Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Caro Terrazas, I., Castillo Fernandez, R., 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., Devitt, A., Diurba, R., Domine, L., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Escudero Sanchez, L., Evans, J. J., 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., Hall, E., Hamilton, P., Hen, O., Horton-Smith, G. A., Hourlier, A., Huang, E. -C., Itay, R., James, C., Jan de Vries, J., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. -J., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, B., Kirby, M., Kobilarcik, T., Kreslo, I., LaZur, R., Lepetic, I., Li, K., Li, Y., Littlejohn, B. R., Lorca, D., Louis, W. C., Luo, X., Marchionni, A., Marcocci, S., Mariani, C., Marsden, D., Marshall, J., Martin-Albo, 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., Mousseau, J., Murphy, M., Naples, D., Navrer-Agasson, A., Neely, R. K., Nienaber, P., Nowak, J., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I. D., Porzio, D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rodriguez Rondon, J., Rogers, H. E., Rosenberg, M., Ross-Lonergan, M., Russell, B., Scanavini, G., Schmitz, D. W., Schukraft, A., Shaevitz, M. H., Sharankova, R., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Söldner-Rembold, S., Soleti, S. R., Spentzouris, P., Spitz, J., Stancari, M., John, J. St., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Toups, M., Tsai, Y. -T., Tufanli, S., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wu, W., Yang, T., Yarbrough, G., Yates, L. E., Zeller, G. P., Zennamo, J., Zhang, C., & MicroBooNE Collaboration. Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber.  United States.  https://doi.org/10.1103/PhysRevD.103.092003

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                    Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., Bolton, T., Camilleri, L., Caratelli, D., Caro Terrazas, I., Castillo Fernandez, R., 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., Devitt, A., Diurba, R., Domine, L., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Ereditato, A., Escudero Sanchez, L., Evans, J. J., 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., Hall, E., Hamilton, P., Hen, O., Horton-Smith, G. A., Hourlier, A., Huang, E. -C., Itay, R., James, C., Jan de Vries, J., Ji, X., Jiang, L., Jo, J. H., Johnson, R. A., Jwa, Y. -J., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, B., Kirby, M., Kobilarcik, T., Kreslo, I., LaZur, R., Lepetic, I., Li, K., Li, Y., Littlejohn, B. R., Lorca, D., Louis, W. C., Luo, X., Marchionni, A., Marcocci, S., Mariani, C., Marsden, D., Marshall, J., Martin-Albo, 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., Mousseau, J., Murphy, M., Naples, D., Navrer-Agasson, A., Neely, R. K., Nienaber, P., Nowak, J., Palamara, O., Paolone, V., Papadopoulou, A., Papavassiliou, V., Pate, S. F., Paudel, A., Pavlovic, Z., Piasetzky, E., Ponce-Pinto, I. D., Porzio, D., Prince, S., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rodriguez Rondon, J., Rogers, H. E., Rosenberg, M., Ross-Lonergan, M., Russell, B., Scanavini, G., Schmitz, D. W., Schukraft, A., Shaevitz, M. H., Sharankova, R., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Söldner-Rembold, S., Soleti, S. R., Spentzouris, P., Spitz, J., Stancari, M., John, J. St., Strauss, T., Sutton, K., Sword-Fehlberg, S., Szelc, A. M., Tagg, N., Tang, W., Terao, K., Thorpe, C., Toups, M., Tsai, Y. -T., Tufanli, S., Uchida, M. A., Usher, T., Van De Pontseele, W., Viren, B., Weber, M., Wei, H., Williams, Z., Wolbers, S., Wongjirad, T., Wospakrik, M., Wu, W., Yang, T., Yarbrough, G., Yates, L. E., Zeller, G. P., Zennamo, J., Zhang, C., and MicroBooNE Collaboration. Fri .  
"Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber".  United States.  https://doi.org/10.1103/PhysRevD.103.092003.

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@article{osti_1782986,

  title        = {Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber},

  author       = {Abratenko, P. and Alrashed, M. and An, R. and Anthony, J. and Asaadi, J. and Ashkenazi, A. and Balasubramanian, S. and Baller, B. and Barnes, C. and Barr, G. 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 Camilleri, L. and Caratelli, D. and Caro Terrazas, I. and Castillo Fernandez, R. 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 Devitt, A. and Diurba, R. and Domine, L. and Dorrill, R. and Duffy, K. and Dytman, S. and Eberly, B. and Ereditato, A. and Escudero Sanchez, L. and Evans, J. J. 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 Hall, E. and Hamilton, P. and Hen, O. and Horton-Smith, G. A. and Hourlier, A. and Huang, E. -C. and Itay, R. and James, C. and Jan de Vries, J. and Ji, X. and Jiang, L. and Jo, J. H. and Johnson, R. A. and Jwa, Y. -J. and Kamp, N. and Karagiorgi, G. and Ketchum, W. and Kirby, B. and Kirby, M. and Kobilarcik, T. and Kreslo, I. and LaZur, R. and Lepetic, I. and Li, K. and Li, Y. and Littlejohn, B. R. and Lorca, D. and Louis, W. C. and Luo, X. and Marchionni, A. and Marcocci, S. and Mariani, C. and Marsden, D. and Marshall, J. and Martin-Albo, 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 Mousseau, J. and Murphy, M. and Naples, D. and Navrer-Agasson, A. and Neely, R. K. and Nienaber, P. and Nowak, J. and Palamara, O. and Paolone, V. and Papadopoulou, A. and Papavassiliou, V. and Pate, S. F. and Paudel, A. and Pavlovic, Z. and Piasetzky, E. and Ponce-Pinto, I. D. and Porzio, 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 Rochester, L. and Rodriguez Rondon, J. and Rogers, H. E. and Rosenberg, M. and Ross-Lonergan, M. and Russell, B. and Scanavini, G. and Schmitz, D. W. and Schukraft, A. and Shaevitz, M. H. and Sharankova, R. 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 Stancari, M. and John, J. St. 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 Toups, M. and Tsai, Y. -T. and Tufanli, S. 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 Wu, W. and Yang, T. and Yarbrough, G. and Yates, L. E. and Zeller, G. P. and Zennamo, J. and Zhang, C. and MicroBooNE Collaboration},

  abstractNote = {We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an interaction includes an e–, γ, μ–, π±, and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE [Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017)]. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE’s deep-learning-based νe search analysis. In this paper, we present the network’s design, training, and performance on simulation and data from the MicroBooNE detector.},

  doi          = {10.1103/PhysRevD.103.092003},

  journal      = {Physical Review D},

  number       = 9,

  volume       = 103,

  place        = {United States},

  year         = {Fri May 14 00:00:00 EDT 2021},

  month        = {Fri May 14 00:00:00 EDT 2021}

}

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Journal of Instrumentation, Vol. 12, Issue 03
DOI: 10.1088/1748-0221/12/03/T03003

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

A method to determine the electric field of liquid argon time projection chambers using a UV laser system and its application in MicroBooNE
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Adams, C.; Alrashed, M.; An, R.
Journal of Instrumentation, Vol. 15, Issue 07
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Measurement of neutrino-induced neutral-current coherent $π^{0}$ production in the NOvA near detector
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Acero, M. A.; Adamson, P.; Aliaga, L.
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DOI: 10.1103/PhysRevD.102.012004

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Title: Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber

Abstract

Citation Formats

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