Reddening (E(B-V)) Map (Green et al. 2019)
HEALPix nested, res 10 (Nside=1024)
3.4 to 13.7 arcmin
Original Data Source
Green et al. (2019) derived a new three-dimensional map of dust reddening to a distance of several kpc. The map covers the sky north of declination -30 degrees, mostly at a resolution of 3.4 to 13.7 arcminutes. It is based on Pan-STARRS 1 photometry of 800 million stars, 2MASS photometry of 200 million stars, and Gaia parallaxes of 500 million stars. The new map is referred to as Bayestar19 and supersedes the previous Bayestar15 and Bayestar17 maps of Green et al. (2015) and Green et al. (2018).
This new map contains three major improvements over our previous work. First, the inclusion of Gaia parallaxes dramatically improves distance estimates to nearby stars. Second, we incorporate a spatial prior that correlates the dust density across nearby sightlines. This produces a smoother map, with more isotropic clouds and smaller distance uncertainties, particularly to clouds within the nearest kiloparsec. Third, we infer the dust density with a distance resolution that is four times finer than in our previous work, to accommodate the improvements in signal-to-noise enabled by the other improvements.
The 3D dust mapping site of Green et al. gives instructions for downloading or querying the three-dimensional data and gives notes on the data.
LAMBDA provides a two-dimensional map of the cumulative reddening to the farthest distance bin for each pixel.
This was calculated as the median over the Markov chain samples in the 3D dataset for the farthest distance bin,
following the method given here. We also provide a 1-sigma uncertainty for the cumulative reddening. Following a recommendation from Gregory Green,
this was calculated as half of the difference between the 84th percentile cumulative reddening sample and 16th percentile sample, with a floor of 0.03.
For Galactic plane, other 3D extinction maps may also be of interest. See Chen et al. 2018 and references therein.
More details can be found in:
"A 3D Dust Map Based on Gaia, Pan-STARRS 1 and 2MASS", Green, Schlafly, Zucker, et al. 2019, arXiv:1905.02734
3D Dust Mapping site
Dataverse Bayestar site