This article has an erratum: [https://doi.org/10.1051/0004-6361/201935093e]
Volume 631, November 2019
|Number of page(s)||15|
|Section||Interstellar and circumstellar matter|
|Published online||15 October 2019|
Charting nearby dust clouds using Gaia data only★
Max Planck Institute for Astrophysics,
2 Ludwig-Maximilians-Universität, Geschwister-Scholl Platz 1, 80539 Munich, Germany
Accepted: 18 July 2019
Aims. Highly resolved maps of the local Galactic dust are an important ingredient for sky emission models. Over almost the whole electromagnetic spectrum one can see imprints of dust, many of which originate from dust clouds within 300 pc. Having a detailed 3D reconstruction of these local dust clouds enables detailed studies, helps to quantify the impact on other observables, and is a necessary milestone of larger reconstructions, as every sightline for more distant objects will pass through the local dust.
Methods. To infer the dust density we use parallax and extinction estimates published by the Gaia collaboration in their second data release (DR2). We model the dust as a log-normal process using a hierarchical Bayesian model. We also nonparametrically infer the kernel of the log-normal process, which corresponds to the physical spatial correlation power spectrum of the log-density.
Results. Using only data from Gaia DR2, we reconstruct the 3D dust density and its spatial correlation spectrum in a 600 pc cube centered on the Sun. We report a spectral index of the logarithmic dust density of 3.1 on Fourier scales with wavelengths between 2 and 125 pc. The resulting 3D dust map as well as the power spectrum and posterior samples are publicly available for download.
Key words: dust, extinction / local insterstellar matter / methods: data analysis / solar neighborhood
The 3D dust absorption maps are also available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (18.104.22.168) or via http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/631/A32
© R. H. Leike and T. A. Enßlin 2019
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Open Access funding provided by Max Planck Society.
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