Neutrino Direction Reconstruction using a CNN for GeV Scale Neutrinos in IceCube

The IceCube Neutrino Observatory is designed to observe neutrinos interacting deep within the South Pole ice. It consists of 5,160 digital optical modules, which are arrayed over a cubic kilometer from 1,450 m to 2,450 m depth. At the center of the array is a subdetector, DeepCore. It has a denser configuration which lowers the observable energy threshold to about 10 GeV and creates the opportunity to study neutrino oscillations with low energy atmospheric neutrinos. A precise reconstruction of neutrino direction is critical in the measurements of oscillation parameters. In this poster, I will present a method to reconstruct the zenith angle of 10-GeV scale events in IceCube by using a convolutional neural network (CNN). Compared to the current likelihood-based reconstruction algorithm, the CNN method shows improvements in both angular resolution and processing speed.