Identification of Patterns in Cosmic-Ray Arrival Directions using Dynamic Graph Convolutional Neural Networks

Autor:	Bister, Teresa, Erdmann, Martin, Glombitza, Jonas, Langner, Niklas, Schulte, Josina, Wirtz, Marcus
Rok vydání:	2020
Předmět:	Astrophysics - High Energy Astrophysical Phenomena High Energy Physics - Experiment
Druh dokumentu:	Working Paper
DOI:	10.1016/j.astropartphys.2020.102527
Popis:	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. Comment: 15 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.13038 Zobrazit plný text záznamu View this record from Arxiv