A Bayesian self-clustering analysis of the highest energy cosmic rays detected by the Pierre Auger Observatory

作者：Khanin Alexander^*; Mortlock Daniel J

来源：Monthly Notices of the Royal Astronomical Society, 2014, 444(2): 1591-1599.

DOI：10.1093/mnras/stu1517

• methods: statistical
• cosmic rays

Cosmic rays are protons and atomic nuclei that flow into our Solar system and reach the Earth with energies of up to similar to 10(21) eV. The sources of ultrahigh energy cosmic rays (UHECRs) with E greater than or similar to 10(19) eV remain unknown, although there are theoretical reasons to think that at least some come from active galactic nuclei (AGNs). One way to assess the different hypotheses is by analysing the arrival directions of UHECRs, in particular their self-clustering. We have developed a fully Bayesian approach to analysing the self-clustering of points on the sphere, which we apply to the UHECR arrival directions. The analysis is based on a multistep approach that enables the application of Bayesian model comparison to cases with weak prior information. We have applied this approach to the 69 highest energy events recorded by the Pierre Auger Observatory, which is the largest current UHECR data set. We do not detect self-clustering, but simulations show that this is consistent with the AGN-sourced model for a data set of this size. Data sets of several hundred UHECRs would be sufficient to detect clustering in the AGN model. Samples of this magnitude are expected to be produced by future experiments, such as the Japanese Experiment Module Extreme Universe Space Observatory.

• 出版日期2014-10-21