  The IceCube Neutrino Observatory, located under 1.4 km of Antarctic ice,
instruments a cubic kilometer of ice with 5,160 optical modules that detect
Cherenkov radiation originating from neutrino interactions. The more densely
instrumented center, DeepCore, aims to detect atmospheric neutrinos at 10-GeV
scales to improve important measurements of fundamental neutrino properties
such as the oscillation parameters and to search for non-standard interactions.
Sensitivity to oscillation parameters, dependent on the distance traveled over
the neutrino energy (L/E), is limited in IceCube by the resolution of the
arrival angle (which determines L) and energy (E). Event reconstruction
improvements can therefore directly lead to advancements in oscillation
results. This work uses a Convolutional Neural Network (CNN) to reconstruct the
energy of 10-GeV scale neutrino events in IceCube, providing results with
competitive resolutions and faster runtimes than previous likelihood-based
methods.
