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The Galactic Faraday rotation sky 2020

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Hutschenreuter,  S.
Computational Structure Formation, MPI for Astrophysics, Max Planck Society;

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Enßlin,  T. A.
Cosmology, MPI for Astrophysics, Max Planck Society;

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Citation

Hutschenreuter, S., Anderson, C. S., Betti, S., Bower, G. C., Brown, J.-A., Brüggen, M., et al. (2022). The Galactic Faraday rotation sky 2020. Astronomy and Astrophysics, 657: A43. doi:10.1051/0004-6361/202140486.


Cite as: http://hdl.handle.net/21.11116/0000-0009-EFFA-1
Abstract
Aims. This work provides an update to existing reconstructions of the Galactic Faraday rotation sky by processing almost all Faraday rotation data sets available at the end of the year 2020. Observations of extra-Galactic sources in recent years have further illuminated the previously underconstrained southern celestial sky, as well as parts of the inner disc of the Milky Way, along with other regions. This has culminated in an all-sky data set of 55 190 data points, thereby comprising a significant expansion on the 41 330 used in previous works. At the same time, this novelty makes an updated separation of the Galactic component a promising enterprise. The increased source density allows us to present our results in a resolution of about 1.3 × 10−2 deg2 (46.8 arcmin2), which is a twofold increase compared to previous works. Methods. As for previous Faraday rotation sky reconstructions, this work is based on information field theory, namely, a Bayesian inference scheme for field-like quantities that handles noisy and incomplete data. Results. In contrast to previous reconstructions, we find a significantly thinner and pronounced Galactic disc with small-scale structures exceeding values of several thousand rad m−2. The improvements can mainly be attributed to the new catalog of Faraday data, but are also supported by advances in correlation structure modeling within numerical information field theory. We also provide a detailed discussion on the statistical properties of the Faraday rotation sky and we investigate correlations with other data sets.