Explaining the UHECR spectrum, composition and large-scale anisotropies with radio galaxies

TL;DR

This paper investigates whether radio galaxies can explain the observed ultrahigh energy cosmic ray spectrum, composition, and anisotropies by modeling their flux contributions and source configurations, fitting observational data effectively.

Contribution

It introduces a comprehensive model combining local and non-local radio galaxy contributions to UHECRs, constrained by radio-CR correlation and evolution, to explain spectrum and anisotropies.

Findings

Few local sources like Fornax A and Virgo A dominate above the ankle.

Low-luminosity radio galaxies contribute isotropic background below the ankle.

The model fits observational data well with a small number of local sources.

Abstract

Radio galaxies are promising candidates as the sources of ultrahigh energy cosmic rays (UHECRs). In this work, we examine if the stringent constraints imposed by the dipole and quadropole anisotropies as well as the UHECR spectrum and composition allow that radio galaxies are the dominant extragalactic cosmic ray sources. In order to calculate the UHECR flux emitted by individual radio galaxies, we constrain their properties using information from the radio-CR correlation and a dynamical evolution model. In addition to the UHECR flux from individual, local sources, we include the diffuse flux emitted by the bulk of non-local radio galaxies based on their radio luminosity distribution. Analyzing the source parameters within a range around their expected properties, we finally determine the configurations of local sources describing well the UHECR spectrum, composition and large-scale anisotropies. We obtain a good description of all data even in the case that we include only a small number of local sources. In particular, we find that scenarios where few sources like Fornax A and Virgo A dominate the flux above the ankle, while low-luminosity radio galaxies contribute an isotropic background dominating below the ankle, provide a good fit to the data.