Fast Energy Reconstruction using CNNs for GeV Scale Neutrinos in IceCube

The IceCube Neutrino Observatory, located deep under the Antarctic ice, detects astrophysical and atmospheric neutrinos. It uses 5160 optical modules spanning a cubic kilometer of ice to detect Cherenkov radiation originating from neutrino interactions. Atmospheric neutrinos at the scale of 10-GeV can be used to measure important fundamental properties of neutrinos such as the oscillation parameters and to search for non-standard interactions. Current likelihood-based reconstructions take seconds to minutes to reconstruct the properties (energy, direction, etc.) of a neutrino event, which makes them computationally challenging for large data sets. In this talk, I will present work showing the optimization of a convolutional neural network (CNN) to reconstruct the energy of 10-GeV scale events in IceCube. This method takes sub-milliseconds per neutrino event, and also offers improvements in the energy resolution.