Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
High Energy Astrophysical Phenomena
2023-01-19 v2 Instrumentation and Methods for Astrophysics
Data Analysis, Statistics and Probability
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.
Cite
@article{arxiv.2211.17198,
title = {Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks},
author = {Paras Koundal},
journal= {arXiv preprint arXiv:2211.17198},
year = {2023}
}