Speaker
Description
The field of ultra-high-energy cosmic ray (UHECR) astronomy has been facing an ongoing challenge due to the unknown impact of magnetic deflections on the observed events. However, with the recent successes in employing deep neural networks (DNN) to reconstruct data from multiple types of surface detectors, the wider availability of mass-sensitive datasets is expected to arrive within the next few years. More data is expected from the Telescope Array Project x4 (TAx4) and the multi-hybrid observations of the Auger Prime upgrade of the Pierre Auger Observatory (PAO) will especially be crucial in verifying these DNN-inferred quantities.
Our work aims to develop a new approach to UHECR astronomy by measuring the properties of UHECR sources using mass-sensitive data that encompasses the most recent knowledge of the acceleration and propagation mechanism of UHECRs. We constructed a Bayesian hierarchical framework that utilises the spatial, energy, and mass composition information from available data to infer the source properties such as luminosity or the spectral index as well as the strength of the extragalactic magnetic field while marginalising the uncertainties of the detector or unknown environmental (latent) parameters. In our work, we model the mass-dependent spatial deflections from the Galactic magnetic field (GMF) on an event-by-event basis using the latest available GMF model (Unger & Farrar 2023, ApJ 970 1, 95) and developed a novel method to determine the nuclear composition of each UHECR source by applying the propagation solver PriNCe (Heinze et al. 2019, ApJ 873 1, 88) that incorporates the energy losses due to photo-nuclear interactions. We apply this new method on realistic simulated datasets with nuclear composition information for PAO and TA to demonstrate the method’s capabilities of the Bayesian inference of source parameters. It is shown that leveraging the full three-dimensional mass-sensitive data while incorporating the most accurate physical models of UHECR acceleration and propagation allows a more unbiased reconstruction of source properties and extragalactic magnetic field strengths.
