ﻻ يوجد ملخص باللغة العربية
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.
We propose a machine learning method to investigate the propagation of cosmic rays, based on the precisely measured spectra of primary and secondary nuclei Li, Be, B, C, and O by AMS-02, ACE, and Voyager-1. We train two Convolutional Neural Network m
Energy-dependent patterns in the arrival directions of cosmic rays are searched for using data of the Pierre Auger Observatory. We investigate local regions around the highest-energy cosmic rays with $E geq 6 cdot 10^{19}$ eV by analyzing cosmic rays
The sources of ultrahigh-energy cosmic rays (UHECRs) have been difficult to catch. It was recently pointed out that while sources of UHECR protons exhibit anisotropy patterns that become denser and compressed with rising energy, nucleus-emitting-sour
Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 56
We present a novel method to search for structures of coherently aligned patterns in ultra-high energy cosmic-ray arrival directions simultaneously across the entire sky. This method can be used to obtain information on the Galactic magnetic field, i