Academic Journal
Classification of electron and muon neutrino events for the ESSνSB near water Cherenkov detector using Graph Neural Networks
| Τίτλος: | Classification of electron and muon neutrino events for the ESSνSB near water Cherenkov detector using Graph Neural Networks |
|---|---|
| Συγγραφείς: | Aguilar, J., Zormpa, O., Bolling, B., Burgman, A., Carlile, C. J., Cederkall, J., Christiansen, P., Collins, M., Danared, H., Eshraqi, M., Iversen, K. E., Lindroos, M., Park, J., et al. |
| Συνεισφορές: | Lund University, Faculty of Science, Department of Physics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Originator, Lund University, Faculty of Science, Department of Physics, Astrophysics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Astrofysik, Originator, Lund University, Faculty of Science, Department of Physics, Particle and nuclear physics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Partikel- och kärnfysik, Originator |
| Πηγή: | Journal of Instrumentation. 20(8) |
| Θεματικοί όροι: | Natural Sciences, Physical Sciences, Subatomic Physics, Naturvetenskap, Fysik, Subatomär fysik |
| Περιγραφή: | In the effort to obtain a precise measurement of leptonic CP-violation with the ESSνSB 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 types of events. The GNN flavour classification of neutrino interaction events results in a true positive rate of 85.87 % (57.90 %) for muon (electron) neutrinos, compared to 35.55 % (0.21 %) for the likelihood-based method with identical constraints on the false positive rate, while the reconstruction speed is increased by a factor of 104. 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. |
| Σύνδεσμος πρόσβασης: | https://doi.org/10.1088/1748-0221/20/08/P08030 |
| Βάση Δεδομένων: | SwePub |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.1088/1748-0221/20/08/P08030# Name: EDS - SwePub (ns324271) Category: fullText Text: View record in SwePub |
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| Items | – Name: Title Label: Title Group: Ti Data: Classification of electron and muon neutrino events for the ESSνSB near water Cherenkov detector using Graph Neural Networks – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aguilar%2C+J%2E%22">Aguilar, J.</searchLink><br /><searchLink fieldCode="AR" term="%22Zormpa%2C+O%2E%22">Zormpa, O.</searchLink><br /><searchLink fieldCode="AR" term="%22Bolling%2C+B%2E%22">Bolling, B.</searchLink><br /><searchLink fieldCode="AR" term="%22Burgman%2C+A%2E%22">Burgman, A.</searchLink><br /><searchLink fieldCode="AR" term="%22Carlile%2C+C%2E+J%2E%22">Carlile, C. J.</searchLink><br /><searchLink fieldCode="AR" term="%22Cederkall%2C+J%2E%22">Cederkall, J.</searchLink><br /><searchLink fieldCode="AR" term="%22Christiansen%2C+P%2E%22">Christiansen, P.</searchLink><br /><searchLink fieldCode="AR" term="%22Collins%2C+M%2E%22">Collins, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Danared%2C+H%2E%22">Danared, H.</searchLink><br /><searchLink fieldCode="AR" term="%22Eshraqi%2C+M%2E%22">Eshraqi, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Iversen%2C+K%2E+E%2E%22">Iversen, K. E.</searchLink><br /><searchLink fieldCode="AR" term="%22Lindroos%2C+M%2E%22">Lindroos, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Park%2C+J%2E%22">Park, J.</searchLink><br /><searchLink fieldCode="AR" term="%22et+al%2E%22">et al.</searchLink> – Name: Author Label: Contributors Group: Au Data: Lund University, Faculty of Science, Department of Physics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Originator<br />Lund University, Faculty of Science, Department of Physics, Astrophysics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Astrofysik, Originator<br />Lund University, Faculty of Science, Department of Physics, Particle and nuclear physics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Partikel- och kärnfysik, Originator – Name: TitleSource Label: Source Group: Src Data: <i>Journal of Instrumentation</i>. 20(8) – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Natural+Sciences%22">Natural Sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+Sciences%22">Physical Sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Subatomic+Physics%22">Subatomic Physics</searchLink><br /><searchLink fieldCode="DE" term="%22Naturvetenskap%22">Naturvetenskap</searchLink><br /><searchLink fieldCode="DE" term="%22Fysik%22">Fysik</searchLink><br /><searchLink fieldCode="DE" term="%22Subatomär+fysik%22">Subatomär fysik</searchLink> – Name: Abstract Label: Description Group: Ab Data: In the effort to obtain a precise measurement of leptonic CP-violation with the ESSνSB 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 types of events. The GNN flavour classification of neutrino interaction events results in a true positive rate of 85.87 % (57.90 %) for muon (electron) neutrinos, compared to 35.55 % (0.21 %) for the likelihood-based method with identical constraints on the false positive rate, while the reconstruction speed is increased by a factor of 104. 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. – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://doi.org/10.1088/1748-0221/20/08/P08030" linkWindow="_blank">https://doi.org/10.1088/1748-0221/20/08/P08030</link> |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1088/1748-0221/20/08/P08030 Languages: – Text: English Subjects: – SubjectFull: Natural Sciences Type: general – SubjectFull: Physical Sciences Type: general – SubjectFull: Subatomic Physics Type: general – SubjectFull: Naturvetenskap Type: general – SubjectFull: Fysik Type: general – SubjectFull: Subatomär fysik Type: general Titles: – TitleFull: Classification of electron and muon neutrino events for the ESSνSB near water Cherenkov detector using Graph Neural Networks Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aguilar, J. – PersonEntity: Name: NameFull: Zormpa, O. – PersonEntity: Name: NameFull: Bolling, B. – PersonEntity: Name: NameFull: Burgman, A. – PersonEntity: Name: NameFull: Carlile, C. J. – PersonEntity: Name: NameFull: Cederkall, J. – PersonEntity: Name: NameFull: Christiansen, P. – PersonEntity: Name: NameFull: Collins, M. – PersonEntity: Name: NameFull: Danared, H. – PersonEntity: Name: NameFull: Eshraqi, M. – PersonEntity: Name: NameFull: Iversen, K. E. – PersonEntity: Name: NameFull: Lindroos, M. – PersonEntity: Name: NameFull: Park, J. – PersonEntity: Name: NameFull: et al. – PersonEntity: Name: NameFull: Lund University, Faculty of Science, Department of Physics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Originator – PersonEntity: Name: NameFull: Lund University, Faculty of Science, Department of Physics, Astrophysics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Astrofysik, Originator – PersonEntity: Name: NameFull: Lund University, Faculty of Science, Department of Physics, Particle and nuclear physics, Lunds universitet, Naturvetenskapliga fakulteten, Fysiska institutionen, Partikel- och kärnfysik, Originator IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 17480221 – Type: issn-locals Value: SWEPUB_FREE – Type: issn-locals Value: LU_SWEPUB Numbering: – Type: volume Value: 20 – Type: issue Value: 8 Titles: – TitleFull: Journal of Instrumentation Type: main |
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