Charting nearby dust clouds using Gaia data only
Aims: Highly resolved maps of the local Galactic dust are an important ingredient for sky emission models. In nearly the whole electromagnetic spectrum one can see imprints of dust, many of which originate from dust clouds within 300pc. Having a detailed 3D reconstruction of these local dust clouds...
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description | Aims: Highly resolved maps of the local Galactic dust are an important ingredient for sky emission models. In nearly the whole electromagnetic spectrum one can see imprints of dust, many of which originate from dust clouds within 300pc. Having a detailed 3D reconstruction of these local dust clouds enables detailed studies, helps to quantify the impact on other observables and is a milestone necessary to enable 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 absorption estimates published by the Gaia collaboration in their second data release. We model the dust as a log-normal process using a hierarchical Bayesian model. We also infer non-parametrically the kernel of the log-normal process, which corresponds to the physical spatial correlation power spectrum of the log-density. Results: Using only Gaia data of the second Gaia data release, we reconstruct the 3D dust density and its spatial correlation spectrum in a 600pc cube centered on the Sun. We report a spectral index of the logarithmic dust density of \(3.1\) on Fourier scales with wavelengths between 2pc and 125pc. The resulting 3D dust map as well as the power spectrum and posterior samples are publicly available for download. |
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In nearly the whole electromagnetic spectrum one can see imprints of dust, many of which originate from dust clouds within 300pc. Having a detailed 3D reconstruction of these local dust clouds enables detailed studies, helps to quantify the impact on other observables and is a milestone necessary to enable 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 absorption estimates published by the Gaia collaboration in their second data release. We model the dust as a log-normal process using a hierarchical Bayesian model. We also infer non-parametrically the kernel of the log-normal process, which corresponds to the physical spatial correlation power spectrum of the log-density. Results: Using only Gaia data of the second Gaia data release, we reconstruct the 3D dust density and its spatial correlation spectrum in a 600pc cube centered on the Sun. We report a spectral index of the logarithmic dust density of \(3.1\) on Fourier scales with wavelengths between 2pc and 125pc. The resulting 3D dust map as well as the power spectrum and posterior samples are publicly available for download.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1901.05971</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Astronomical models ; Bayesian analysis ; Clouds ; Cosmic dust ; Density ; Dust ; Parallax ; Physics - Astrophysics of Galaxies ; Physics - Solar and Stellar Astrophysics</subject><ispartof>arXiv.org, 2019-07</ispartof><rights>2019. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27925</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.1901.05971$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1051/0004-6361/201935093$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Leike, R H</creatorcontrib><creatorcontrib>Enßlin, T A</creatorcontrib><title>Charting nearby dust clouds using Gaia data only</title><title>arXiv.org</title><description>Aims: Highly resolved maps of the local Galactic dust are an important ingredient for sky emission models. In nearly the whole electromagnetic spectrum one can see imprints of dust, many of which originate from dust clouds within 300pc. Having a detailed 3D reconstruction of these local dust clouds enables detailed studies, helps to quantify the impact on other observables and is a milestone necessary to enable 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 absorption estimates published by the Gaia collaboration in their second data release. We model the dust as a log-normal process using a hierarchical Bayesian model. We also infer non-parametrically the kernel of the log-normal process, which corresponds to the physical spatial correlation power spectrum of the log-density. Results: Using only Gaia data of the second Gaia data release, we reconstruct the 3D dust density and its spatial correlation spectrum in a 600pc cube centered on the Sun. We report a spectral index of the logarithmic dust density of \(3.1\) on Fourier scales with wavelengths between 2pc and 125pc. The resulting 3D dust map as well as the power spectrum and posterior samples are publicly available for download.</description><subject>Astronomical models</subject><subject>Bayesian analysis</subject><subject>Clouds</subject><subject>Cosmic dust</subject><subject>Density</subject><subject>Dust</subject><subject>Parallax</subject><subject>Physics - Astrophysics of Galaxies</subject><subject>Physics - Solar and Stellar Astrophysics</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj0tLw0AUhQdBsNT-AFcOuE6887gzyVKKVqHgpvswT02JSZ1JxPx7-3B1FufjcD5C7hiUskKER5N-25-S1cBKwFqzK7LgQrCikpzfkFXOewDgSnNEsSCw_jRpbPsP2geT7Ez9lEfqumHymU75VGxMa6g3o6FD38235DqaLofVfy7J7uV5t34ttu-bt_XTtjDIVcHR2ZoF7hSLTqBToBGiBYzSWSNU9EwLL6AOtqqCdVxzJaNE5RGMQyaW5P4ye9ZpDqn9MmluTlrNWetIPFyIQxq-p5DHZj9MqT9-ajhTNdNaciX-AKbjTpA</recordid><startdate>20190717</startdate><enddate>20190717</enddate><creator>Leike, R H</creator><creator>Enßlin, T A</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>GOX</scope></search><sort><creationdate>20190717</creationdate><title>Charting nearby dust clouds using Gaia data only</title><author>Leike, R H ; Enßlin, T A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a526-25cb91e2c61fc35c60750fb05f4cba36fd173d309eb88ebc27264f456d50ac513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Astronomical models</topic><topic>Bayesian analysis</topic><topic>Clouds</topic><topic>Cosmic dust</topic><topic>Density</topic><topic>Dust</topic><topic>Parallax</topic><topic>Physics - Astrophysics of Galaxies</topic><topic>Physics - Solar and Stellar Astrophysics</topic><toplevel>online_resources</toplevel><creatorcontrib>Leike, R H</creatorcontrib><creatorcontrib>Enßlin, T A</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leike, R H</au><au>Enßlin, T A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Charting nearby dust clouds using Gaia data only</atitle><jtitle>arXiv.org</jtitle><date>2019-07-17</date><risdate>2019</risdate><eissn>2331-8422</eissn><abstract>Aims: Highly resolved maps of the local Galactic dust are an important ingredient for sky emission models. In nearly the whole electromagnetic spectrum one can see imprints of dust, many of which originate from dust clouds within 300pc. Having a detailed 3D reconstruction of these local dust clouds enables detailed studies, helps to quantify the impact on other observables and is a milestone necessary to enable 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 absorption estimates published by the Gaia collaboration in their second data release. We model the dust as a log-normal process using a hierarchical Bayesian model. We also infer non-parametrically the kernel of the log-normal process, which corresponds to the physical spatial correlation power spectrum of the log-density. Results: Using only Gaia data of the second Gaia data release, we reconstruct the 3D dust density and its spatial correlation spectrum in a 600pc cube centered on the Sun. We report a spectral index of the logarithmic dust density of \(3.1\) on Fourier scales with wavelengths between 2pc and 125pc. The resulting 3D dust map as well as the power spectrum and posterior samples are publicly available for download.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.1901.05971</doi><oa>free_for_read</oa></addata></record> |
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subjects | Astronomical models Bayesian analysis Clouds Cosmic dust Density Dust Parallax Physics - Astrophysics of Galaxies Physics - Solar and Stellar Astrophysics |
title | Charting nearby dust clouds using Gaia data only |
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