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Classification of Electron and Muon Neutrino Events for the ESS$ν$SB Near Water Cherenkov Detector using Graph Neural Networks

Fuente: arXiv

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Bibliographic Details
Main Authors:	Aguilar, J., Anastasopoulos, M., Barčot, D., Baussan, E., Bhattacharyya, A. K., Bignami, A., Blennow, M., Bogomilov, M., Bolling, B., Bouquerel, E., Bramati, F., Branca, A., Brunetti, G., Burgman, A., Bustinduy, I., Carlile, C. J., Cederkall, J., Choi, T. W., Choubey, S., Christiansen, P., Collins, M., Morales, E. Cristaldo, Cupiał, P., D'Ago, D., Danared, H., de André, J. P. A. M., Dracos, M., Efthymiopoulos, I., Ekelöf, T., Eshraqi, M., Fanourakis, G., Farricker, A., Fasoula, E., Fukuda, T., García-Marcos, J., Gazis, N., Geralis, Th., Ghosh, M., Giarnetti, A., Gokbulut, G., Hagner, C., Halić, L., Hooft, M., Iversen, K. E., Jachowicz, N., Jenssen, M., Johansson, R., Karakoulias, I., Kasimi, E., Topaksu, A. Kayis, Kildetoft, B., Kliček, B., Kordas, K., Leisos, A., Lindroos, M., Longhin, A., Maiano, C., Marangoni, S., Marrelli, C., Meloni, D., Mezzetto, M., Milas, N., Muñoz, J. L., Niewczas, K., Oglakci, M., Ohlsson, T., Olvegård, M., Pari, M., Park, J., Patrzalek, D., Petkov, G., Petridou, Ch., Poussot, P., Psallidas, A., Pupilli, F., Saiang, D., Sampsonidis, D., Scanu, A., Schwab, C., Sordo, F., Sosa, A., Stavropoulos, G., Stipčević, M., Tarkeshian, R., Terranova, F., Tolba, T., Trachanas, E., Tsenov, R., Tsirigotis, A., Tzamarias, S. E., Vanderpoorten, M., Vankova-Kirilova, G., Vassilopoulos, N., Vihonen, S., Wurtz, J., Zeter, V., Zormpa, O.
Format:	Preprint
Published:	2025
Subjects:	High Energy Physics - Experiment Instrumentation and Detectors
Online Access:	Acceder al recurso
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author	Aguilar, J. Anastasopoulos, M. Barčot, D. Baussan, E. Bhattacharyya, A. K. Bignami, A. Blennow, M. Bogomilov, M. Bolling, B. Bouquerel, E. Bramati, F. Branca, A. Brunetti, G. Burgman, A. Bustinduy, I. Carlile, C. J. Cederkall, J. Choi, T. W. Choubey, S. Christiansen, P. Collins, M. Morales, E. Cristaldo Cupiał, P. D'Ago, D. Danared, H. de André, J. P. A. M. Dracos, M. Efthymiopoulos, I. Ekelöf, T. Eshraqi, M. Fanourakis, G. Farricker, A. Fasoula, E. Fukuda, T. García-Marcos, J. Gazis, N. Geralis, Th. Ghosh, M. Giarnetti, A. Gokbulut, G. Hagner, C. Halić, L. Hooft, M. Iversen, K. E. Jachowicz, N. Jenssen, M. Johansson, R. Karakoulias, I. Kasimi, E. Topaksu, A. Kayis Kildetoft, B. Kliček, B. Kordas, K. Leisos, A. Lindroos, M. Longhin, A. Maiano, C. Marangoni, S. Marrelli, C. Meloni, D. Mezzetto, M. Milas, N. Muñoz, J. L. Niewczas, K. Oglakci, M. Ohlsson, T. Olvegård, M. Pari, M. Park, J. Patrzalek, D. Petkov, G. Petridou, Ch. Poussot, P. Psallidas, A. Pupilli, F. Saiang, D. Sampsonidis, D. Scanu, A. Schwab, C. Sordo, F. Sosa, A. Stavropoulos, G. Stipčević, M. Tarkeshian, R. Terranova, F. Tolba, T. Trachanas, E. Tsenov, R. Tsirigotis, A. Tzamarias, S. E. Vanderpoorten, M. Vankova-Kirilova, G. Vassilopoulos, N. Vihonen, S. Wurtz, J. Zeter, V. Zormpa, O.
author_facet	Aguilar, J. Anastasopoulos, M. Barčot, D. Baussan, E. Bhattacharyya, A. K. Bignami, A. Blennow, M. Bogomilov, M. Bolling, B. Bouquerel, E. Bramati, F. Branca, A. Brunetti, G. Burgman, A. Bustinduy, I. Carlile, C. J. Cederkall, J. Choi, T. W. Choubey, S. Christiansen, P. Collins, M. Morales, E. Cristaldo Cupiał, P. D'Ago, D. Danared, H. de André, J. P. A. M. Dracos, M. Efthymiopoulos, I. Ekelöf, T. Eshraqi, M. Fanourakis, G. Farricker, A. Fasoula, E. Fukuda, T. García-Marcos, J. Gazis, N. Geralis, Th. Ghosh, M. Giarnetti, A. Gokbulut, G. Hagner, C. Halić, L. Hooft, M. Iversen, K. E. Jachowicz, N. Jenssen, M. Johansson, R. Karakoulias, I. Kasimi, E. Topaksu, A. Kayis Kildetoft, B. Kliček, B. Kordas, K. Leisos, A. Lindroos, M. Longhin, A. Maiano, C. Marangoni, S. Marrelli, C. Meloni, D. Mezzetto, M. Milas, N. Muñoz, J. L. Niewczas, K. Oglakci, M. Ohlsson, T. Olvegård, M. Pari, M. Park, J. Patrzalek, D. Petkov, G. Petridou, Ch. Poussot, P. Psallidas, A. Pupilli, F. Saiang, D. Sampsonidis, D. Scanu, A. Schwab, C. Sordo, F. Sosa, A. Stavropoulos, G. Stipčević, M. Tarkeshian, R. Terranova, F. Tolba, T. Trachanas, E. Tsenov, R. Tsirigotis, A. Tzamarias, S. E. Vanderpoorten, M. Vankova-Kirilova, G. Vassilopoulos, N. Vihonen, S. Wurtz, J. Zeter, V. Zormpa, O.
contents	In the effort to obtain a precise measurement of leptonic CP-violation with the ESS$ν$SB experiment, accurate and fast reconstruction of detector events plays a pivotal role. In this work, we examine the possibility of replacing the currently proposed likelihood-based reconstruction method with an approach based on Graph Neural Networks (GNNs). As the likelihood-based reconstruction method is reasonably accurate but computationally expensive, one of the benefits of a Machine Learning (ML) based method is enabling fast event reconstruction in the detector development phase, allowing for easier investigation of the effects of changes to the detector design. Focusing on classification of flavour and interaction type in muon and electron events and muon- and electron neutrino interaction events, we demonstrate that the GNN reconstructs events with greater accuracy than the likelihood method for events with greater complexity, and with increased speed for all events. Additionally, we investigate the key factors impacting reconstruction performance, and demonstrate how separation of events by pion production using another GNN classifier can benefit flavour classification.
format	Preprint
id	arxiv_https___arxiv_org_abs_2503_15247
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Classification of Electron and Muon Neutrino Events for the ESS$ν$SB Near Water Cherenkov Detector using Graph Neural Networks Aguilar, J. Anastasopoulos, M. Barčot, D. Baussan, E. Bhattacharyya, A. K. Bignami, A. Blennow, M. Bogomilov, M. Bolling, B. Bouquerel, E. Bramati, F. Branca, A. Brunetti, G. Burgman, A. Bustinduy, I. Carlile, C. J. Cederkall, J. Choi, T. W. Choubey, S. Christiansen, P. Collins, M. Morales, E. Cristaldo Cupiał, P. D'Ago, D. Danared, H. de André, J. P. A. M. Dracos, M. Efthymiopoulos, I. Ekelöf, T. Eshraqi, M. Fanourakis, G. Farricker, A. Fasoula, E. Fukuda, T. García-Marcos, J. Gazis, N. Geralis, Th. Ghosh, M. Giarnetti, A. Gokbulut, G. Hagner, C. Halić, L. Hooft, M. Iversen, K. E. Jachowicz, N. Jenssen, M. Johansson, R. Karakoulias, I. Kasimi, E. Topaksu, A. Kayis Kildetoft, B. Kliček, B. Kordas, K. Leisos, A. Lindroos, M. Longhin, A. Maiano, C. Marangoni, S. Marrelli, C. Meloni, D. Mezzetto, M. Milas, N. Muñoz, J. L. Niewczas, K. Oglakci, M. Ohlsson, T. Olvegård, M. Pari, M. Park, J. Patrzalek, D. Petkov, G. Petridou, Ch. Poussot, P. Psallidas, A. Pupilli, F. Saiang, D. Sampsonidis, D. Scanu, A. Schwab, C. Sordo, F. Sosa, A. Stavropoulos, G. Stipčević, M. Tarkeshian, R. Terranova, F. Tolba, T. Trachanas, E. Tsenov, R. Tsirigotis, A. Tzamarias, S. E. Vanderpoorten, M. Vankova-Kirilova, G. Vassilopoulos, N. Vihonen, S. Wurtz, J. Zeter, V. Zormpa, O. High Energy Physics - Experiment Instrumentation and Detectors In the effort to obtain a precise measurement of leptonic CP-violation with the ESS$ν$SB experiment, accurate and fast reconstruction of detector events plays a pivotal role. In this work, we examine the possibility of replacing the currently proposed likelihood-based reconstruction method with an approach based on Graph Neural Networks (GNNs). As the likelihood-based reconstruction method is reasonably accurate but computationally expensive, one of the benefits of a Machine Learning (ML) based method is enabling fast event reconstruction in the detector development phase, allowing for easier investigation of the effects of changes to the detector design. Focusing on classification of flavour and interaction type in muon and electron events and muon- and electron neutrino interaction events, we demonstrate that the GNN reconstructs events with greater accuracy than the likelihood method for events with greater complexity, and with increased speed for all events. Additionally, we investigate the key factors impacting reconstruction performance, and demonstrate how separation of events by pion production using another GNN classifier can benefit flavour classification.
title	Classification of Electron and Muon Neutrino Events for the ESS$ν$SB Near Water Cherenkov Detector using Graph Neural Networks
topic	High Energy Physics - Experiment Instrumentation and Detectors
url	https://arxiv.org/abs/2503.15247