Electron Neutrino Energy Reconstruction in NOvA Using CNN Particle IDs (1910.06953v2)

Abstract: NOvA is a long-baseline neutrino oscillation experiment. It is optimized 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 (CNN) to identify neutrino events in two functionally identical liquid scintillator detectors. A different network, called prong-CNN, 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 prong-CNN 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 parameters measurement. This paper describes the methods to identify particles with prong-CNN and the following approach to estimate $\nu_e$ energy for signal events.