Graph Neural Networks for low-energy event classification & reconstruction in IceCube ... - Abbasi, R.; Ackermann, M.; Adams, J.; Aggarwal, N.; Aguilar, J.A.; Ahlers, M.; Ahrens, M.; Alameddine, J.M.; Alves, A.A.; Amin, N.M.; Andeen, K.; Anderson, T.; et al. (Humboldt-Universität zu Berlin, 2022)
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 ...