Neutrino Energy Reconstruction with Regression Convolutional Neural Network at DUNE

The study of neutrino oscillations is one of the primary physics goals of the DUNE experiment. The neutrino oscillations are functions of neutrino energies, hence the reconstruction of neutrino energies with high resolution is important to accomplish the successful measurements. We had developed a method to reconstruct energy taking the sum of the reconstructed track or shower and hadronic energies. This method can be limited because of the complicated event topology, low hadronic energy resolution, and invisible energy. Thus, we developed regression Convolutional Neural Networks (CNNs) to estimate electron neutrino energy with deconvoluted waveform inputs. Compared with the kinematics-based reconstruction, this method shows a significantly better energy resolution. In this talk, I will describe the methods to reconstruct neutrino energies with kinematic-based and regression CNN-based reconstructions at DUNE.