arXiv · 2211.17198
Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
Abstract
The IceCube Neutrino Observatory is a multi-component detector embedded deep within the South-Pole Ice. This proceeding will discuss an analysis from an integrated operation of IceCube and its surface array, IceTop, to estimate cosmic-ray composition. The work will describe a novel graph neural network based approach for estimating the mass of primary cosmic rays, that takes advantage of signal-footprint information and reconstructed cosmic-ray air shower parameters. In addition, the work will also introduce new composition-sensitive parameters for improving the estimation of cosmic-ray composition, with the potential of improving our understanding of the high-energy muon content in cosmic-ray air showers.
Explore related subjects
Keep this discovery
Paras Koundal. 2022-11-30. Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks. https://arxiv.org/abs/2211.17198
Cite the original work for its findings. Save a collection to share your selection of sources.