A Bayesian Framework for UHECR Source Association and Parameter Inference

TL;DR

This paper introduces a Bayesian framework utilizing energy and mass moments of logarithmic mass to improve source association and parameter inference for ultra-high-energy cosmic rays, accounting for magnetic deflections and propagation effects.

Contribution

The work develops a novel Bayesian method that uses energy and mass moments to infer UHECR source parameters, including composition, with demonstrated performance on simulated data.

Findings

Effective in constraining source parameters using simulated datasets.

Can be applied to current observational data from Pierre Auger and Telescope Array.

Provides insights into UHECR source identification and composition.

Abstract

The identification of potential sources of ultra-high-energy cosmic rays (UHECRs) remains challenging due to magnetic deflections and propagation losses, which are particularly strong for nuclei. In previous iterations of this work, we proposed an approach for UHECR astronomy based on Bayesian inference through explicit modelling of propagation and magnetic deflection effects. The event-by-event mass information is expected to provide tighter constraints on these parameters and to help identify unknown sources. However, the measurements of the average mass through observations from the surface detectors at the Pierre Auger Observatory already indicate that the UHECR masses are well represented through its statistical average. In this contribution, we present our framework which uses energy and mass moments of $\ln A$ to infer the source parameters of UHECRs, including the mass composition at the source. We demonstrate the performance of our model using simulated datasets based on the Pierre Auger Observatory and Telescope Array Project. Our model can be readily applied to currently available data, and we discuss the implications of our results for UHECR source identification.