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Classification of Electron and Muon Neutrino Events for the ESSνSB Near Water Cherenkov Detector using Graph Neural Networks

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arxiv 2503.15247 v4 pith:5A5RJVRG submitted 2025-03-19 hep-ex physics.ins-det

Classification of Electron and Muon Neutrino Events for the ESSνSB Near Water Cherenkov Detector using Graph Neural Networks

J. Aguilar , M. Anastasopoulos , D. Bar\v{c}ot , E. Baussan , A.K. Bhattacharyya , A. Bignami , M. Blennow , M. Bogomilov
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B. Bolling E. Bouquerel F. Bramati A. Branca G. Brunetti A. Burgman I. Bustinduy C.J. Carlile J. Cederkall T. W. Choi S. Choubey P. Christiansen M. Collins E. Cristaldo Morales P. Cupia\l D. D'Ago H. Danared J. P. A. M. de Andr\'e M. Dracos I. Efthymiopoulos T. Ekel\"of M. Eshraqi G. Fanourakis A. Farricker E. Fasoula T. Fukuda J. Garc\'ia-Marcos N. Gazis Th. Geralis M. Ghosh A. Giarnetti G. Gokbulut C. Hagner L. Hali\'c M. Hooft K. E. Iversen N. Jachowicz M. Jenssen R. Johansson I. Karakoulias E. Kasimi A. Kayis Topaksu B. Kildetoft B. Kli\v{c}ek K. Kordas A. Leisos M. Lindroos A. Longhin C. Maiano S. Marangoni C. Marrelli D. Meloni M. Mezzetto N. Milas J.L. Mu\~noz K. Niewczas M. Oglakci T. Ohlsson M. Olveg\r{a}rd M. Pari J. Park D. Patrzalek G. Petkov Ch. Petridou P. Poussot A. Psallidas F. Pupilli D. Saiang D. Sampsonidis A. Scanu C. Schwab F. Sordo A. Sosa G. Stavropoulos M. Stip\v{c}evi\'c R. Tarkeshian F. Terranova T. Tolba E. Trachanas R. Tsenov A. Tsirigotis S. E. Tzamarias M. Vanderpoorten G. Vankova-Kirilova N. Vassilopoulos S. Vihonen J. Wurtz V. Zeter O. Zormpa
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classification hep-ex physics.ins-det
keywords eventsreconstructiondetectormethodclassificationelectronaccuratedemonstrate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In the effort to obtain a precise measurement of leptonic CP-violation with the ESS$\nu$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.

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