Speaker
Description
The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation large (20 kton) liquid-scintillator neutrino detector, which is designed to determine the neutrino mass ordering from its precise reactor neutrino energy spectrum measurement. For reactor antineutrino detection, it is necessary to precisely eliminate cosmogenic backgrounds like 9Li/8He and fast neutrons that are generated by cosmic muons. This can be achieved by applying muon veto cuts, where accurate muon track and shower vertex reconstruction could largely increase the effective detection volume of JUNO’s central detector. This poster presents a machine learning approach for the track and shower vertex reconstruction for single muon events. This approach shows promising reconstruction performance based on the Monte Carlo simulations.
