Speaker
Description
The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to measure the neutrino mass ordering (NMO) using a 20-kton liquid scintillator (LS) detector. Besides the precise measurement of the reactor neutrinos oscillation spectrum, an atmospheric neutrino oscillation measurement in JUNO offers independent sensitivity for NMO, which can potentially increase JUNO’s total sensitivity in a combined analysis. Reconstruction of atmospheric neutrinos in large unsegmented LS detectors like JUNO, including the event directionality, energy, and neutrino flavors, however, is very challenging. In particular, reconstruction of atmospheric neutrino events’ directionality has never been performed in any such detectors before.
In this contribution, we present a novel multi-purpose reconstruction method for atmospheric neutrinos in JUNO at few-GeV based on the machine learning technique. This method extracts features related to event topology from PMT waveforms and use them as inputs to machine learning models. A preliminary study based on the JUNO simulation shows good reconstruction performances for event directionality, energy, vertex, and particle ID. This method also has a great application potential for similar LS detectors.
| Consider for long presentation | Yes |
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