Electron Neutrino Energy Reconstruction in NOvA Using CNN Particle IDs
This work addresses the need for more accurate neutrino energy estimation in particle physics experiments, but it is incremental as it builds on existing CNN methods for particle identification.
The paper tackles the problem of electron neutrino energy reconstruction in the NOvA experiment by using a convolutional neural network (CNN) to classify particles as lepton or hadron, which improves the resolution and systematic robustness of the energy estimator for neutrino oscillation measurements.
NOvA is a long-baseline neutrino oscillation experiment. It is optimized to measure $ν_e$ appearance and $ν_μ$ disappearance at the Far Detector in the $ν_μ$ 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 $ν_e$ energy for signal events.