Guilt by Association: Finding Cosmic Ray Sources Using Hierarchical Bayesian Clustering

TL;DR

This paper introduces a Bayesian hierarchical clustering method to identify cosmic ray sources by analyzing their directional data and associations, accounting for uncertainties and sky exposure.

Contribution

It develops a novel Bayesian hierarchical model that compares source association hypotheses and estimates astrophysical parameters from cosmic ray data.

Findings

Effective in distinguishing source associations

Incorporates directional uncertainties and sky exposure

Learns astrophysical parameters from data

Abstract

The Earth is continuously showered by charged cosmic ray particles, naturally produced atomic nuclei moving with velocity close to the speed of light. Among these are ultra high energy cosmic ray particles with energy exceeding 5x10^19 eV, which is ten million times more energetic than the most energetic particles produced at the Large Hadron Collider. Astrophysical questions include: what phenomenon accelerates particles to such high energies, and what sort of nuclei are energized? Also, the magnetic deflection of the trajectories of the cosmic rays makes them potential probes of galactic and intergalactic magnetic fields. We develop a Bayesian hierarchical model that can be used to compare different association models between the cosmic rays and source population, using Bayes factors. A measurement model with directional uncertainties and accounting for non-uniform sky exposure is incoporated into the model. The methodology allows us to learn about astrophysical parameters, such as those governing the source luminosity function and the cosmic magnetic field.