Speaker
Description
The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation large (20 kton) liquid-scintillator neutrino detector, designed to determine the neutrino mass ordering from its precise reactor neutrino spectrum measurement. Additionally, high-energy (GeV-level) atmospheric neutrino measurements could also improve its sensitivity to mass ordering via matter effects on oscillations, which depend on the directional (zenith angle) resolution of the incident neutrino. However, large unsegmented liquid scintillator detectors like JUNO are traditionally limited in their capabilities of measuring event directionality.
This contribution presents a machine learning approach for the directional reconstruction of atmospheric neutrinos in JUNO, which can be applied to other liquid scintillator detectors as well. In this method, several features relevant to event directionality is extracted from PMT waveforms and used as inputs to the machine learning models. Three independent models are used to perform reconstruction, each with its own unique approach for handling the same input features. Preliminary results based on Monte Carlo simulation show promising potential for this approach.
| Type of contribution | Talk: 15 minutes. |
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