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

TL;DR

This paper introduces a CNN-based method for reconstructing the zenith angle of GeV-scale neutrinos in IceCube, aiming to improve directional accuracy over existing likelihood-based algorithms.

Contribution

The study presents a novel CNN approach for neutrino direction reconstruction in IceCube, demonstrating its effectiveness compared to traditional likelihood methods.

Findings

CNN achieves comparable or better angular resolution

Improves reconstruction speed over likelihood methods

Enhances neutrino oscillation measurements accuracy

Abstract

The IceCube Neutrino Observatory observes neutrinos interacting deep within the South Pole ice. It consists of 5,160 digital optical modules embedded within a cubic kilometer of ice, over depths of 1,450 m to 2,450 m. At the lower center of the array is the DeepCore subdetector. Its denser sensor configuration lowers the observable energy threshold to the GeV-scale, facilitating the study of atmospheric neutrino oscillations. The precise reconstruction of neutrino direction is critical in the measurements of oscillation parameters. This work presents a method to reconstruct the zenith angle of GeV-scale events in IceCube by using a convolutional neural network and compares the result to that of the current likelihood-based reconstruction algorithm.