Elemental Composition of Cosmic Rays : Analysis of IceCube data using Graph Neural Networks

Cosmic rays (CRs) are high-energy ionized nuclei that emanate from astrophysical sources and regularly bombard Earth's atmosphere. The IceCube Neutrino Observatory (ICNO), a cubic-kilometer observatory embedded in South-Pole Antarctic ice, is very well suited to investigate CR in the energy regime where the transition from Galactic to the extra-galactic origin of CR occurs. This work delves into the analysis of the elemental composition at ICNO using the footprint of extensive air showers (EAS) detected at IceTop (IT) (the surface detector component of IceCube) in conjunction with the in-ice component. Using dedicated Monte Carlo simulations, the work leverages energy deposits by TeV-muons (within EASs) in IceCube, to devise various CR composition-sensitive physics observables. In addition to providing composition-sensitivity, the observables also provide the possibility for testing internal consistencies in phenomenological models which describe hadronic interactions of CR with atmospheric-nuclei.\par
An extended part of this work focuses on developing a Graph Neural Network (GNN)-based approach, informed by the physics of EASs. ... mehrThis approach utilizes the observed footprint of EAS at both IceTop and IceCube to estimate logarithmic mass for each individual EAS. As a first-of-its-kind endeavor, the GNN architecture incorporates multiple EAS-physics-inspired inductive-biases to obtain the mass estimate in an efficient manner. Moreover, in order to provide enhanced stability to the network the work also adapts ideas from other leading fields in Deep Learning. In addition to the physics-informed application of the GNN, the network also benefits from the composition-sensitive observables developed in this work, along with other shower observables, to provide enhanced composition sensitivity. A gradient-boosted decision tree-based approach is used for the energy estimate. The different Machine Learning (ML) methods are concurrently utilized to estimate the elemental composition of CRs. The methods developed on simulations are tested and validated with a burnsample of real data. Finally the work presents the elemental composition of real-data (using 10$\%$ of 2012-data, spread throughout the year) observed at ICNO, obtaining fractional spectra for four primary groups (proton, Helium, Oxygen, and Iron). The fractions obtained are validated using distributions of EAS-observables in real data.
This work makes fractional estimates of primary types which indicate a heavier composition than the prior work at ICNO. This has strong physics implications as it might indicate the presence of the highest-energy galactic CR in the transition region between Galactic to the extragalactic origin of CR. A future update of this work, incorporating greater statistics and including quantified detector systematics, has the potential to enhance the physics significance of the results obtained from this work. This work also serves as a very useful test case for using GNNs in the planned upgrade of ICNO, namely IceCube-Gen2. In addition, to that this work also establishes GNNs as a very useful tool for exploring unique problems in astroparticle physics.