Cosmic-Ray Composition with IceTop and IceCube using graph neural networks

Le résumé fourni par la source

The IceCube Neutrino Observatory, along with its surface array IceTop, is a unique instrument for identifying the elemental composition of cosmic rays around the transition region between Galactic and extragalactic origin of cosmic rays. It can thus provide valuable insights into identifying astrophysical sources of high-energy particles. This work reports the preliminary cosmic-ray composition estimate for air showers detected in IceCube. The analysis is performed by an integrated use of a graph neural network (GNN) based approach, along with reconstructed air-shower features. The GNN uses the detector-hits of air showers recorded at IceTop and IceCube, mapped as a graph. The reconstructed features capture multiple aspects of air-shower physics. The implementation of the GNN based approach also provides the flexibility and facilitates the potential incorporation of planned detector extensions in the future.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.