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Author  |
ANTARES Collaboration (Albert, A. et al); Alves, S.; Calvo, D.; Carretero, V.; Gozzini, R.; Hernandez-Rey, J.J.; Lazo, A.; Manczak, J.; Real, D.; Sanchez-Losa, A.; Saina, A.; Salesa Greus, F.; Zornoza, J.D.; Zuñiga, J. |
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Title |
Deep learning framework for enhanced neutrino reconstruction of single-line events in the ANTARES telescope |
Type |
Journal Article |
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Year |
2026 |
Publication |
Machine Learning-Science and Technology |
Abbreviated Journal |
Mach. Learn.-Sci. Technol. |
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Volume |
7 |
Issue |
3 |
Pages |
035004 - 27pp |
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Keywords |
deep neural network; neutrino event reconstruction; submarine telescope; transfer learning; dimensionality reduction; event classification; multimessenger astrophysics |
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Abstract |
We present the N-fit algorithm designed to improve the reconstruction of neutrino events detected by a single line of the ANTARES underwater telescope, usually associated with low energy neutrino events (similar to 100 GeV). N-Fit is a neural network model that relies on deep learning and combines several advanced techniques in machine learning-deep convolutional layers, mixture density output layers, and transfer learning (TL). This framework divides the reconstruction process into two dedicated branches for each neutrino event topology-tracks and showers-composed of sub-models for spatial estimation-direction and position-and energy inference, which later on are combined for event classification. Regarding the direction of single-line (SL) events, the N-Fit algorithm significantly refines the estimation of the zenithal angle, and delivers reliable azimuthal angle predictions that were previously unattainable with traditional chi 2-fit methods. Improving on energy estimation of SL events is a tall order; N-Fit benefits from TL to efficiently integrate key characteristics, such as the estimation of the closest distance from the event to the detector. N-Fit also takes advantage from TL in event topology classification by freezing convolutional layers of the pretrained branches. Tests on Monte Carlo simulations and data demonstrate a significant reduction in mean and median absolute errors across all reconstructed parameters. The improvements achieved by N-Fit highlight its potential for advancing multimessenger astrophysics and enhancing our ability to probe fundamental physics beyond the Standard Model using SL events from ANTARES data. |
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Address |
[Albert, A.; Drouhin, D.; Pradier, T.] Univ Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Email: mardid@fis.upv.es; |
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Publisher |
IOP Publishing Ltd |
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English |
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Notes |
WOS:001752848100001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
7211 |
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