Multilevel Bayesian framework for modeling the production, propagation and detection of ultra-high energy cosmic rays
Kunlaya Soiaporn, David Chernoff, Thomas Loredo, David Ruppert, Ira, Wasserman

TL;DR
This paper introduces a multilevel Bayesian framework with MCMC algorithms to analyze the origins of ultra-high energy cosmic rays, assessing their association with local extragalactic sources like active galactic nuclei.
Contribution
The paper develops a novel Bayesian modeling approach and computational methods for analyzing UHECR data, accounting for measurement errors and source association uncertainties.
Findings
No significant association between UHECRs and local AGN overall.
Nearest AGN, Centaurus A and NGC 4945, show the highest probability of association.
Estimated cosmic magnetic deflection scales are approximately 3° to 30°.
Abstract
Ultra-high energy cosmic rays (UHECRs) are atomic nuclei with energies over ten million times energies accessible to human-made particle accelerators. Evidence suggests that they originate from relatively nearby extragalactic sources, but the nature of the sources is unknown. We develop a multilevel Bayesian framework for assessing association of UHECRs and candidate source populations, and Markov chain Monte Carlo algorithms for estimating model parameters and comparing models by computing, via Chib's method, marginal likelihoods and Bayes factors. We demonstrate the framework by analyzing measurements of 69 UHECRs observed by the Pierre Auger Observatory (PAO) from 2004-2009, using a volume-complete catalog of 17 local active galactic nuclei (AGN) out to 15 megaparsecs as candidate sources. An early portion of the data ("period 1," with 14 events) was used by PAO to set an energy cut…
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