Speaker
Description
Jiangmen Underground Neutrino Observatory (JUNO) is a next generation 20-kton liquid scintillator detector under construction in southern China. It is designed to determine neutrino mass ordering via the measurement of reactor neutrino oscillation, and also to study other physics topics including atmospheric neutrinos, supernova neutrinos and more. The detector's large mass and high photosensor coverage provide an excellent scenario for the application of machine learning techniques. In this contribution, I present the recent progress of machine learning applications in JUNO, including event reconstruction and particle identification etc., which show great potential on enhancing the detector's performance for various physics topics.
Significance
The JUNO detector is a liquid scintillator detector traditionally known for its excellent energy resolution but limited in some other measurements such as the directionality measurement and particle identification in the GeV energy region. This presentation covers works that utilize machine learning methods that significantly enhance the detector's capability, which not only further improve its resolution in various measurements, but also make new physics topics such as atmospheric neutrino oscillation measurement possible. Those methods are applicable to other similar detectors as well.
References
Related publications: PHYS. REV. D 109, 052005 (2024), https://arxiv.org/abs/2503.21353, Eur.Phys.J.C 85 (2025) 1, 69
| Experiment context, if any | Jiangmen Underground Neutrino Observatory (the JUNO experiment) |
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