Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks

https://doi.org/10.1016/j.astropartphys.2020.102527Get rights and content
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Abstract

We present a new approach for the identification of ultra-high energy cosmic rays from sources using dynamic graph convolutional neural networks. These networks are designed to handle sparsely arranged objects and to exploit their short- and long-range correlations. Our method searches for patterns in the arrival directions of cosmic rays, which are expected to result from coherent deflections in cosmic magnetic fields. The network discriminates astrophysical scenarios with source signatures from those with only isotropically distributed cosmic rays and allows for the identification of cosmic rays that belong to a deflection pattern. We use simulated astrophysical scenarios where the source density is the only free parameter to show how density limits can be derived. We apply this method to a public data set from the AGASA Observatory.

Keywords

Ultra-high energy cosmic rays
Sources
Magnetic fields
Neural networks

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