Galactic reddening in 3D from stellar photometry – an improved map

By Gregory M. Green, Edward F. Schlafly, Douglas P. Finkbeiner, Hans-Walter Rix, N. Martin, W. Burgett, P. Draper, H. Flewelling, K. Hodapp, N. Kaiser and 6 more. 2018

333 citations recorded by the source corpus.

We present a new 3D map of interstellar dust reddening, covering three quarters of the sky (declinations of δ ≳ −30°) out to a distance of several kiloparsecs. The map is based on high-quality stellar photometry of 800 million stars from Pan-STARRS 1 and 2MASS. We divide the sky into sightlines containing a few hundred stars each, and then infer stellar distances and types, along with the line-of-sight dust distribution. Our new map incorporates a more accurate average extinction law and an additional 1.5 yr of Pan-STARRS 1 data, tracing dust to greater extinctions and at higher angular resolutions than our previous map. Out of the plane of the Galaxy, our map agrees well with 2D reddening maps derived from far-infrared dust emission. After accounting for a 25 per cent difference in scale, we find a mean scatter of ∼10 per cent between our map and the Planck far-infrared emission-based dust map, out to a depth of 0.8 mag in E(gP1 − rP1), with the level of agreement varying over the sky. Our map can be downloaded at http://argonaut.skymaps.info, or from the Harvard Dataverse (Green 2017).

Subfields: Astronomy and Astrophysics, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/66ce3f2db9f8395ddd8edf5aa514ba8febe5237f

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Citations in the corpus

dustmaps: A Python interface for maps of interstellar dust (2018, neutral): “The left panel is a plot of dust reddening in Green et al. (2018) to a distance of 800 pc, while the right panel shows the correlation between Green et al. (2018) and Planck Collaboration et al. (2014).”
PSR J0952−0607: The Fastest and Heaviest Known Galactic Neutron Star (2022, result): “Extinction in the direction of J0952 is estimated from the three-dimensional dust map of Green et al. (2018), reaching its maximal ( )- = -+E g r 0.05 0.020.03 mag ( = -+A 0.14V 0.050.08 mag) by 0.3 kpc. Treating AV as a free parameter in the MCMC fits, we find 0.16± 0.04 mag, consistent with the…”
The Gaia–Kepler Stellar Properties Catalog. II. Planet Radius Demographics as a Function of Stellar Mass and Age (2020, neutral): “Software:astropy (Astropy Collaboration et al. 2013), dustmaps (Green et al. 2018, 2019), gapfit (Loyd et al. 2020), GNU Parallel (Tange 2018), isoclassify (Huber et al. 2017), KS2D (Taillon 2018), Matplotlib (Hunter 2007), mwdust (Bovy et al. 2016), Pandas (McKinney 2010), scikit-learn (Pedregosa…”
Gaia-2MASS 3D maps of Galactic interstellar dust within 3 kpc (2019, methodology): “In addition to the Green et al. (2018) central profile at the exact coordinates (l,b) we have added their profiles for longitudes l±0.25◦ and b±0.25◦ as well as the average between the five profiles.”
Planck2018 results (2018, methodology): “where E(B − V)R stands for the dust radiance converted to a colour excess using a correlation with extinction to quasars (Planck Collaboration XI 2014), while E(B − V)Green is from a colour excess map (Green et al. 2018) based on Pan-STARRS1 (PS1).”
A compendium of distances to molecular clouds in the Star Formation Handbook (2020, methodology): “…(in the optical and near-infrared bands) as a function of distance, extinction, stellar type, and RV using a technique similar to that outlined in Green et al. (2014, 2015, 2018): m̂ = mint(Mr, [Fe/H]) + AV × (R + RV × R′) + µ (1) Here, mint is the set of intrinsic (un-reddened) magnitudes for…”
Revised Radii of Kepler Stars and Planets Using Gaia Data Release 2 (2018, methodology): “We determined the latter by comparing the Green et al. (2015) map with the Green et al. (2018) map for our sample, yielding a median absolute deviation of ∼ 22%, which we adopt as a fractional uncertainty for our reported extinction values.”
The Revised TESS Input Catalog and Candidate Target List (2019, methodology): “Here, we adopt the newly released threedimensional, empirical, nearly all-sky dust maps from PanSTARRS (Green et al. 2018), which provides an ability to estimate the reddening on a star-by-star basis according to the star’s position in three dimensions (coordinates on the plane of the sky together w”
A Large Catalog of Accurate Distances to Local Molecular Clouds: The Gaia DR2 Edition (2019, neutral): “Software: Astropy (Astropy Collaboration et al. 2018), Dustmaps (Green 2018), Bokeh (Bokeh Development Team 2018), Healpy (Górski et al. 2005), dynesty (Speagle 2018), glue (Robitaille et al. 2017).”
A 3D Dust Map Based on Gaia, Pan-STARRS 1, and 2MASS (2019, methodology): “As in Green et al. (2015, 2018), we put a log-normal prior on the jump in reddening in each distance bin, based on a simple, smooth model of the distribution of dust in the Milky Way.”