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Using rotation measure grids to detect cosmological magnetic fields: A Bayesian approach

Lookup NU author(s): Dr Christopher Hales

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This is the final published version of an article that has been published in its final definitive form by EDP Sciences, 2016.

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Abstract

© ESO, 2016. Determining magnetic field properties in different environments of the cosmic large-scale structure as well as their evolution over redshift is a fundamental step toward uncovering the origin of cosmic magnetic fields. Radio observations permit the study of extragalactic magnetic fields via measurements of the Faraday depth of extragalactic radio sources. Our aim is to investigate how much different extragalactic environments contribute to the Faraday depth variance of these sources. We develop a Bayesian algorithm to distinguish statistically Faraday depth variance contributions intrinsic to the source from those due to the medium between the source and the observer. In our algorithm the Galactic foreground and measurement noise are taken into account as the uncertainty correlations of the Galactic model. Additionally, our algorithm allows for the investigation of possible redshift evolution of the extragalactic contribution. This work presents the derivation of the algorithm and tests performed on mock observations. Because cosmic magnetism is one of the key science projects of the new generation of radio interferometers, we have predicted the performance of our algorithm on mock data collected with these instruments. According to our tests, high-quality catalogs of a few thousands of sources should already enable us to investigate magnetic fields in the cosmic structure.


Publication metadata

Author(s): Vacca V, Oppermann N, Ensslin T, Jasche J, Selig M, Greiner M, Junklewitz H, Reinecke M, Bruggen M, Carretti E, Feretti L, Ferrari C, Hales CA, Horellou C, Ideguchi S, Johnston-Hollitt M, Pizzo RF, Rottgering H, Shimwell TW, Takahashi K

Publication type: Article

Publication status: Published

Journal: Astronomy and Astrophysics

Year: 2016

Volume: 591

Online publication date: 03/06/2017

Acceptance date: 25/03/2016

Date deposited: 30/06/2017

ISSN (print): 0004-6361

ISSN (electronic): 1432-0746

Publisher: EDP Sciences

URL: https://doi.org/10.1051/0004-6361/201527291

DOI: 10.1051/0004-6361/201527291


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