Electron Neutrino Energy Reconstruction with Convolutional Neural Network

NOvA is a long baseline neutrino oscillation experiment. It uses two functionally identical liquid scintillator detectors to measure $\nu_{e}$ appearance and $\nu_{\mu}$ disappearance at the Far Detector in the $\nu_{\mu}$ beam produced by the NuMI facility at Fermilab. \\ NOvA uses a convolutional neural network(CVN) to identify neutrino events. A different network, called ``Prong-CVN'', has been used to classify reconstructed particles in each event as either lepton or hadron. Within each event, hits are clustered into prongs to reconstruct final state particles and these prongs form the input to this new classifier. Classified particle energies are then used as input to an electron neutrino energy estimator. Improving the resolution and systematic robustness of NOvA's energy estimator will improve the sensitivity of the oscillation measurements. In this talk, I will present our methods to identify particles with Prong-CVN and the following approach to estimate $\nu_{e}$ energy for signal events.