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Title: Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Abstract

Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current νe interactions. Therefore, this pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νeμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.

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); Los Alamos National Lab. (LANL), Los Alamos, NM (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), High Energy Physics (HEP); USDOE Office of Science (SC), Basic Energy Sciences (BES)
Contributing Org.:
MicroBooNE Collaboration
OSTI Identifier:
1838305
Alternate Identifier(s):
OSTI ID: 1833287; OSTI ID: 1867504; OSTI ID: 1906300; OSTI ID: 1972675
Report Number(s):
BNL-222573-2022-JAAM; FERMILAB-PUB-21-509-ND; arXiv:2110.13961
Journal ID: ISSN 1748-0221; TRN: US2300977
Grant/Contract Number:  
SC0012704; AC02-07CH11359; AC02-76SF00515; SC0023471; SC0007859
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 17; Journal Issue: 01; 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; wire-cell; pattern recognition; deep neural network

Citation Formats

Abratenko, P., An, R., Anthony, J., Arellano, L., 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., Book, J. Y., Camilleri, L., Caratelli, D., Caro Terrazas, I., Castillo Fernandez, R., Cavanna, F., Cerati, G., Chen, Y., 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., LaZur, R., Lepetic, I., 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., 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, H. W., Zeller, G. P., Zennamo, J., and Zhang, C. Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation. United States: N. p., 2022. Web. doi:10.1088/1748-0221/17/01/p01037.
Abratenko, P., An, R., Anthony, J., Arellano, L., 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., Book, J. Y., Camilleri, L., Caratelli, D., Caro Terrazas, I., Castillo Fernandez, R., Cavanna, F., Cerati, G., Chen, Y., 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., LaZur, R., Lepetic, I., 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., 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, H. W., Zeller, G. P., Zennamo, J., & Zhang, C. Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation. United States. https://doi.org/10.1088/1748-0221/17/01/p01037
Abratenko, P., An, R., Anthony, J., Arellano, L., 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., Book, J. Y., Camilleri, L., Caratelli, D., Caro Terrazas, I., Castillo Fernandez, R., Cavanna, F., Cerati, G., Chen, Y., 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., LaZur, R., Lepetic, I., 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., 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, H. W., Zeller, G. P., Zennamo, J., and Zhang, C. Thu . "Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation". United States. https://doi.org/10.1088/1748-0221/17/01/p01037. https://www.osti.gov/servlets/purl/1838305.
@article{osti_1838305,
title = {Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation},
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 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 Castillo Fernandez, R. and Cavanna, F. and Cerati, G. and Chen, Y. 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 LaZur, R. and Lepetic, I. 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 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, H. W. and Zeller, G. P. and Zennamo, J. and Zhang, C.},
abstractNote = {Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current νe interactions. Therefore, this pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νe (νμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.},
doi = {10.1088/1748-0221/17/01/p01037},
journal = {Journal of Instrumentation},
number = 01,
volume = 17,
place = {United States},
year = {Thu Jan 27 00:00:00 EST 2022},
month = {Thu Jan 27 00:00:00 EST 2022}
}

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