Speakers
Description
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment located in southern China, featuring a 20-kton liquid scintillator detector with excellent energy resolution and large target mass. JUNO has been collecting full liquid scintillator data since August 2025. JUNO has strong potential to observe atmospheric neutrino oscillations. Such measurements would constitute the first observation of atmospheric neutrino oscillations in a large homogeneous liquid scintillator detector and could provide complementary sensitivity to the NMO in a combined analysis with reactor neutrinos.
A key challenge for atmospheric neutrino studies in liquid scintillator detectors is the reconstruction of the neutrino flavor and direction, which are essential for oscillation analyses but are traditionally limited by the isotropic nature of scintillation light.
In this work, we present a machine-learning–based reconstruction framework for atmospheric neutrino events at JUNO that exploits detailed photomultiplier tube (PMT) waveform information. High-level features derived from PMT waveforms, encoding timing, charge evolution, and spatial information, are used as inputs to machine learning models for neutrino direction and flavor reconstruction. Based on this reconstruction method, we also present preliminary performance studies using early JUNO data.