Improved Energy Reconstruction in NOvA with Regression Convolutional Neural Networks

Understand

In neutrino experiments, neutrino energy reconstruction is crucial because neutrino oscillations and differential cross-sections are functions of neutrino energy.

• It is also challenging due to the complexity in the detector response and kinematics of final state particles.
• We propose a regression Convolutional Neural Network (CNN) based method to reconstruct electron neutrino energy and electron energy in the NOvA neutrino experiment.
• We demonstrate that with raw detector pixel inputs, a regression CNN can reconstruct event energy even with complicated final states involving lepton and hadrons.