Speaker
Description
Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation 20-kton liquid scintillator (LS) detector currently under commissioning. JUNO is capable of exploring various physics topics including atmospheric neutrino oscillations. While the atmospheric neutrino flux is initially composed exclusively of muon and electron neutrinos, a large number of them are expected to oscillate to tau neutrinos according to the three-flavor neutrino oscillation hypothesis. However, the current neutrino oscillation studies predominantly focus on muon and electron neutrino measurements, and tau neutrino appearance measurements remain relatively limited due to its high energy threshold, dominated by Cherenkov detectors such as Super-K and IceCube. Searching for atmospheric tau neutrino appearance in JUNO can help verifying the three-flavor oscillation hypothesis and expand the application of LS detectors. This poster introduces the effort to identify and reconstruct atmospheric tau neutrinos in JUNO with a machine learning method. This method extracts features from PMT waveforms and uses them as inputs to machine learning models that are trained to identify tau neutrino interactions and reconstruct their direction and energy. Preliminary study with Monte Carlo simulation demonstrates the feasibility to conduct the tau neutrino appearance search in JUNO.
