Abstract The IceCube Neutrino Observatory is designed to observe neutrinos interacting deep within the South Pole ice sheet. It consists of 5160 digital optical modules, which are arrayed over a cubic kilometer from 1450 m to 2450 m depth. At the lower center of the array is the DeepCore subdetector. 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 contribution, I will discuss a method to reconstruct the zenith angle of 10-GeV scale events in IceCube using a convolutional neural network and compare the result to that of the current likelihood-based reconstruction algorithm.
This content will become publicly available on November 1, 2023
Graph Neural Networks for low-energy event classification & reconstruction in IceCube
Abstract IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared more »
- Authors:
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publication Date:
- NSF-PAR ID:
- 10400232
- Journal Name:
- Journal of Instrumentation
- Volume:
- 17
- Issue:
- 11
- Page Range or eLocation-ID:
- P11003
- ISSN:
- 1748-0221
- Sponsoring Org:
- National Science Foundation
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