Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks
Glombitza J, Erdmann M, Langner N, Bister T, Wirtz M, Schulte J (2021)
Publication Type: Journal article, Original article
Publication year: 2021
Journal
Book Volume: 126
Article Number: 102527
URI: https://www.sciencedirect.com/science/article/pii/S0927650520300992
DOI: 10.1016/j.astropartphys.2020.102527
Open Access Link: https://www.sciencedirect.com/science/article/pii/S0927650520300992
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.
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Germany (DE)How to cite
APA:
Glombitza, J., Erdmann, M., Langner, N., Bister, T., Wirtz, M., & Schulte, J. (2021). Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks. Astroparticle Physics, 126. https://doi.org/10.1016/j.astropartphys.2020.102527
MLA:
Glombitza, Jonas, et al. "Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks." Astroparticle Physics 126 (2021).
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