Speaker
Description
The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator detector with the primary goal of determining the neutrino mass ordering. The suppression of cosmogenic backgrounds, such as $^{9}\text{Li}/^{8}\text{He}$, is critical for achieving JUNO's sensitivity. Since these backgrounds are highly correlated with muon trajectories in both space and time, precise muon track reconstruction is a prerequisite for implementing efficient veto strategies while maximizing the detector’s live-time.
In this poster, we present a comprehensive overview of the muon reconstruction strategies developed within the JUNO collaboration. Both the traditional methods utilizing PMT charge clusters or timing information and advanced machine learning methods are presented. In particular, a novel data-driven machine learning reconstruction framework is developed utilizing high-precision tracks from the Top Tracker (TT) as an external reference for model training and validation. The muon reconstruction performances of various methods are evaluated both by comparisons with the TT tracks and the spatial and temporal correlations between the reconstructed muon tracks and subsequent events, such as spallation neutrons in real data. The results demonstrate the JUNO detector's capability in high-precision muon reconstruction and cosmogenic background suppression and the potential of modern deep learning techniques in large-scale neutrino experiments.