Neutrino Energy Estimation using CNNs in the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment that is designed to probe the neutrino mass hierarchy and mixing structure by looking for a $\nu_{e}$ ($\bar{\nu}_{e}$) appearance signal. It uses two functionally identical liquid scintillator detectors $14.6$ mrad off-axis from the NuMI beamline at Fermilab, allowing for a tightly focused $\nu_{\mu}$ flux peaked at around 2 GeV. In order to make oscillation parameter measurements with high precision, it is important to reconstruct neutrino energies with good resolution as the oscillation probability is a function of neutrino energy. This is not straightforward due to complicated event topologies and large uncertainties on the underlying interaction models. To address this, NOvA has developed a deep learning based CNN that is able to estimate $\nu_{e}$ energies non-parametrically. This approach not only gives superior energy resolutions to traditional kinematic-based estimations, but also shows better behavior under changes to the interaction model; thus enabling us to reduce systematic uncertainties on the final measurement. In this talk, I shall present a summary of the CNN approach and highlight its response to the underlying physical model.