Deep-Learning-Based Kinematic Reconstruction for DUNE

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment based on liquid argon TPC (LArTPC) technology. While LArTPC technology provides excellent spatial resolution, high neutrino detection efficiency, and superb background rejection, it poses significant reconstruction challenges. Deep learning methods, in particular Convolutional Neural Networks (CNNs), have been successfully used in classification problems such as particle identification in DUNE and other neutrino experiments. However, deep learning methods for regression problems, such as the reconstruction of neutrino energies and final state particle momenta, have not yet been developed. Here we design, train, and test two CNN-based methods, using 2-D and 3-D data, for the reconstruction of final state particle direction and energy, as well as neutrino energy. Combining our models with particle masses yields a fully AI-based reconstruction chain producing the four-vector momenta of the final state particles. Compared to a traditional method, our models show considerable performance improvements for both ν_e and ν_μ scenarios.