Deep Learning for Signal and Background Discrimination in Liquid based Neutrino Experiment

In high energy physics experiments, efficient data analysis tools are required to extract interesting information from the massive data. For large-scale liquid-based neutrino experiments, neutrino signals are usually overwhelmed in huge backgrounds. By constructing a liquid neutrino detector toy model, we generate simulation data in Geant4 [1] and run reconstruction for signal background discrimination. The low-level Photo Multipliers (PMT) hits are also projected to a 2D plane to create visualization outputs for classification. With the 2D images as input, we use the Convolutional Neural Network (CNN) as a specific application, which has shown remarkable performance in signal and background discrimination and outperforms those with high-level reconstruction outputs. The method is expected to be used in the neutrino experiments such as JUNO with further study. (paper)