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Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE

Fuente: arXiv

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Auteurs principaux:	MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Book, J. Y., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fine, R., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lane, N., Lepetic, I., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, H., Louis, W. C., Luo, X., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Tang, W., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., Zhang, C.
Format:	Preprint
Publié:	2024
Sujets:	High Energy Physics - Experiment Instrumentation and Detectors
Accès en ligne:	Acceder al recurso
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_version_	1866909224096759808
author	MicroBooNE collaboration Abratenko, P. Alterkait, O. Aldana, D. Andrade Arellano, L. Asaadi, J. Ashkenazi, A. Balasubramanian, S. Baller, B. Barnard, A. Barr, G. Barrow, D. Barrow, J. Basque, V. Bateman, J. Rodrigues, O. Benevides Berkman, S. Bhanderi, A. Bhat, A. Bhattacharya, M. Bishai, M. Blake, A. Bogart, B. Bolton, T. Book, J. Y. Brunetti, M. B. Camilleri, L. Cao, Y. Caratelli, D. Cavanna, F. Cerati, G. Chappell, A. Chen, Y. Conrad, J. M. Convery, M. Cooper-Troendle, L. Crespo-Anadon, J. I. Cross, R. Del Tutto, M. Dennis, S. R. Detje, P. Diurba, R. Djurcic, Z. Dorrill, R. Duffy, K. Dytman, S. Eberly, B. Englezos, P. Ereditato, A. Evans, J. J. Fine, R. Fleming, B. T. Foreman, W. Franco, D. Furmanski, A. P. Gao, F. Garcia-Gamez, D. Gardiner, S. Ge, G. Gollapinni, S. Gramellini, E. Green, P. Greenlee, H. Gu, L. Gu, W. Guenette, R. Guzowski, P. Hagaman, L. Hen, O. Hilgenberg, C. Horton-Smith, G. A. Imani, Z. Irwin, B. Ismail, M. S. James, C. Ji, X. Jo, J. H. Johnson, R. A. Jwa, Y. J. Kalra, D. Kamp, N. Karagiorgi, G. Ketchum, W. Kirby, M. Kobilarcik, T. Kreslo, I. Lane, N. Lepetic, I. Li, J. -Y. Li, Y. Lin, K. Littlejohn, B. R. Liu, H. Louis, W. C. Luo, X. Mariani, C. Marsden, D. Marshall, J. Martinez, N. Caicedo, D. A. Martinez Martynenko, S. Mastbaum, A. Mawby, I. McConkey, N. Meddage, V. Mendez, J. Micallef, J. Miller, K. Mistry, K. Mohayai, T. Mogan, A. Mooney, M. Moor, A. F. Moore, C. D. Lepin, L. Mora Moudgalya, M. M. Babu, S. Mulleria Naples, D. Navrer-Agasson, A. Nayak, N. Nebot-Guinot, M. Nowak, J. Oza, N. Palamara, O. Pallat, N. Paolone, V. Papadopoulou, A. Papavassiliou, V. Parkinson, H. Pate, S. F. Patel, N. Pavlovic, Z. Piasetzky, E. Pletcher, K. Pophale, I. Qian, X. Raaf, J. L. Radeka, V. Rafique, A. Reggiani-Guzzo, M. Ren, L. Rochester, L. Rondon, J. Rodriguez Rosenberg, M. Ross-Lonergan, M. Safa, I. Scanavini, G. Schmitz, D. W. Schukraft, A. Seligman, W. Shaevitz, M. H. Sharankova, R. Shi, J. Snider, E. L. Soderberg, M. Soldner-Rembold, S. Spitz, J. Stancari, M. John, J. St. Strauss, T. Szelc, A. M. Tang, W. Taniuchi, N. Terao, K. Thorpe, C. Torbunov, D. Totani, D. Toups, M. Trettin, A. Tsai, Y. -T. Tyler, J. Uchida, M. A. Usher, T. Viren, B. Weber, M. Wei, H. White, A. J. Wolbers, S. Wongjirad, T. Wospakrik, M. Wresilo, K. Wu, W. Yandel, E. Yang, T. Yates, L. E. Yu, H. W. Zeller, G. P. Zennamo, J. Zhang, C.
author_facet	MicroBooNE collaboration Abratenko, P. Alterkait, O. Aldana, D. Andrade Arellano, L. Asaadi, J. Ashkenazi, A. Balasubramanian, S. Baller, B. Barnard, A. Barr, G. Barrow, D. Barrow, J. Basque, V. Bateman, J. Rodrigues, O. Benevides Berkman, S. Bhanderi, A. Bhat, A. Bhattacharya, M. Bishai, M. Blake, A. Bogart, B. Bolton, T. Book, J. Y. Brunetti, M. B. Camilleri, L. Cao, Y. Caratelli, D. Cavanna, F. Cerati, G. Chappell, A. Chen, Y. Conrad, J. M. Convery, M. Cooper-Troendle, L. Crespo-Anadon, J. I. Cross, R. Del Tutto, M. Dennis, S. R. Detje, P. Diurba, R. Djurcic, Z. Dorrill, R. Duffy, K. Dytman, S. Eberly, B. Englezos, P. Ereditato, A. Evans, J. J. Fine, R. Fleming, B. T. Foreman, W. Franco, D. Furmanski, A. P. Gao, F. Garcia-Gamez, D. Gardiner, S. Ge, G. Gollapinni, S. Gramellini, E. Green, P. Greenlee, H. Gu, L. Gu, W. Guenette, R. Guzowski, P. Hagaman, L. Hen, O. Hilgenberg, C. Horton-Smith, G. A. Imani, Z. Irwin, B. Ismail, M. S. James, C. Ji, X. Jo, J. H. Johnson, R. A. Jwa, Y. J. Kalra, D. Kamp, N. Karagiorgi, G. Ketchum, W. Kirby, M. Kobilarcik, T. Kreslo, I. Lane, N. Lepetic, I. Li, J. -Y. Li, Y. Lin, K. Littlejohn, B. R. Liu, H. Louis, W. C. Luo, X. Mariani, C. Marsden, D. Marshall, J. Martinez, N. Caicedo, D. A. Martinez Martynenko, S. Mastbaum, A. Mawby, I. McConkey, N. Meddage, V. Mendez, J. Micallef, J. Miller, K. Mistry, K. Mohayai, T. Mogan, A. Mooney, M. Moor, A. F. Moore, C. D. Lepin, L. Mora Moudgalya, M. M. Babu, S. Mulleria Naples, D. Navrer-Agasson, A. Nayak, N. Nebot-Guinot, M. Nowak, J. Oza, N. Palamara, O. Pallat, N. Paolone, V. Papadopoulou, A. Papavassiliou, V. Parkinson, H. Pate, S. F. Patel, N. Pavlovic, Z. Piasetzky, E. Pletcher, K. Pophale, I. Qian, X. Raaf, J. L. Radeka, V. Rafique, A. Reggiani-Guzzo, M. Ren, L. Rochester, L. Rondon, J. Rodriguez Rosenberg, M. Ross-Lonergan, M. Safa, I. Scanavini, G. Schmitz, D. W. Schukraft, A. Seligman, W. Shaevitz, M. H. Sharankova, R. Shi, J. Snider, E. L. Soderberg, M. Soldner-Rembold, S. Spitz, J. Stancari, M. John, J. St. Strauss, T. Szelc, A. M. Tang, W. Taniuchi, N. Terao, K. Thorpe, C. Torbunov, D. Totani, D. Toups, M. Trettin, A. Tsai, Y. -T. Tyler, J. Uchida, M. A. Usher, T. Viren, B. Weber, M. Wei, H. White, A. J. Wolbers, S. Wongjirad, T. Wospakrik, M. Wresilo, K. Wu, W. Yandel, E. Yang, T. Yates, L. E. Yu, H. W. Zeller, G. P. Zennamo, J. Zhang, C.
contents	We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.
format	Preprint
id	arxiv_https___arxiv_org_abs_2406_10123
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE MicroBooNE collaboration Abratenko, P. Alterkait, O. Aldana, D. Andrade Arellano, L. Asaadi, J. Ashkenazi, A. Balasubramanian, S. Baller, B. Barnard, A. Barr, G. Barrow, D. Barrow, J. Basque, V. Bateman, J. Rodrigues, O. Benevides Berkman, S. Bhanderi, A. Bhat, A. Bhattacharya, M. Bishai, M. Blake, A. Bogart, B. Bolton, T. Book, J. Y. Brunetti, M. B. Camilleri, L. Cao, Y. Caratelli, D. Cavanna, F. Cerati, G. Chappell, A. Chen, Y. Conrad, J. M. Convery, M. Cooper-Troendle, L. Crespo-Anadon, J. I. Cross, R. Del Tutto, M. Dennis, S. R. Detje, P. Diurba, R. Djurcic, Z. Dorrill, R. Duffy, K. Dytman, S. Eberly, B. Englezos, P. Ereditato, A. Evans, J. J. Fine, R. Fleming, B. T. Foreman, W. Franco, D. Furmanski, A. P. Gao, F. Garcia-Gamez, D. Gardiner, S. Ge, G. Gollapinni, S. Gramellini, E. Green, P. Greenlee, H. Gu, L. Gu, W. Guenette, R. Guzowski, P. Hagaman, L. Hen, O. Hilgenberg, C. Horton-Smith, G. A. Imani, Z. Irwin, B. Ismail, M. S. James, C. Ji, X. Jo, J. H. Johnson, R. A. Jwa, Y. J. Kalra, D. Kamp, N. Karagiorgi, G. Ketchum, W. Kirby, M. Kobilarcik, T. Kreslo, I. Lane, N. Lepetic, I. Li, J. -Y. Li, Y. Lin, K. Littlejohn, B. R. Liu, H. Louis, W. C. Luo, X. Mariani, C. Marsden, D. Marshall, J. Martinez, N. Caicedo, D. A. Martinez Martynenko, S. Mastbaum, A. Mawby, I. McConkey, N. Meddage, V. Mendez, J. Micallef, J. Miller, K. Mistry, K. Mohayai, T. Mogan, A. Mooney, M. Moor, A. F. Moore, C. D. Lepin, L. Mora Moudgalya, M. M. Babu, S. Mulleria Naples, D. Navrer-Agasson, A. Nayak, N. Nebot-Guinot, M. Nowak, J. Oza, N. Palamara, O. Pallat, N. Paolone, V. Papadopoulou, A. Papavassiliou, V. Parkinson, H. Pate, S. F. Patel, N. Pavlovic, Z. Piasetzky, E. Pletcher, K. Pophale, I. Qian, X. Raaf, J. L. Radeka, V. Rafique, A. Reggiani-Guzzo, M. Ren, L. Rochester, L. Rondon, J. Rodriguez Rosenberg, M. Ross-Lonergan, M. Safa, I. Scanavini, G. Schmitz, D. W. Schukraft, A. Seligman, W. Shaevitz, M. H. Sharankova, R. Shi, J. Snider, E. L. Soderberg, M. Soldner-Rembold, S. Spitz, J. Stancari, M. John, J. St. Strauss, T. Szelc, A. M. Tang, W. Taniuchi, N. Terao, K. Thorpe, C. Torbunov, D. Totani, D. Toups, M. Trettin, A. Tsai, Y. -T. Tyler, J. Uchida, M. A. Usher, T. Viren, B. Weber, M. Wei, H. White, A. J. Wolbers, S. Wongjirad, T. Wospakrik, M. Wresilo, K. Wu, W. Yandel, E. Yang, T. Yates, L. E. Yu, H. W. Zeller, G. P. Zennamo, J. Zhang, C. High Energy Physics - Experiment Instrumentation and Detectors We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.
title	Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE
topic	High Energy Physics - Experiment Instrumentation and Detectors
url	https://arxiv.org/abs/2406.10123