A Novel Method Using Generative Neural Networks for Event Reconstruction in Water Cherenkov Detector

Large water Cherenkov detectors are widely used in the detection of neutrinos and nucleon decays. The current approaches to reconstruct events in these detectors involve maximum-likelihood methods in which a likelihood function incorporated with the observed signals on the photosensors is constructed. The likelihood is maximized under different configurations of the event hypothesis to find the best fit for each event. Here we introduce a new way which is based on the traditional likelihood but at the same time employs generative neural networks in place of many templates of traditionally simulated events.  The networks use inputs that characterize an event in the detector and predict probability density distributions for the signals at each photosensor.  The networks are trained with Monte Carlo samples of electrons and muons. We present the initial results from this deep-learning based approach including demonstrations of particle identification and energy reconstruction.