Foreground: Reddening (E(B-V)) Map

The data made available through this page have been superseded by the Green et al. 2018 reddening map, which is available here

Reddening (E(B-V)) Map (Green et al. 2018)

Coordinate System:
Projection Type:
3.4 to 13.7 arcmin arcmin
HEALPix, ring, res 10 (Nside=1024)
Original Data Source:
Download Links:

Green et al. (2018) derived a new three-dimensional map of dust reddening to a distance of several kpc from Pan-STARRS1 and 2MASS stellar photometry. The map covers almost the entire sky north of declination -30 degrees, mostly at a resolution of 3.4 to 13.7 arcminutes. It is based on high quality stellar photometry of 800 million stars from Pan-STARRS 1 and 2MASS. The new map is referred to as Bayestar17 and supersedes the previous Bayestar15 map of Green et al. (2015). The new map incorporates a more accurate average extinction law and an additional 1.5 years of Pan-STARRS 1 data, tracing dust to greater extinctions and at higher angular resolutions than the previous map. The three-dimensional data can be queried or downloaded from the 3D dust mapping site of Green, Schlafly, and Finkbeiner. The site provides the following notes on the three-dimensional data.

  • Units

    The units of Bayestar17 and Bayestar15 differ. This is primarily due to the different extinction laws assumed by the two versions of the dust map. While Bayestar17 assumes the extinction law derived by Schlafly et al. (2016), Bayestar15 relies on the extinction laws of Fitzpatrick (1999) and Cardelli et al. (1989). Both Bayestar17 and Bayestar15 are intended to provide reddening in a similar unit as SFD (Schlegel et al. 1998), which is not quite equal to E(B-V) (see the recalibration of SFD undertaken in Schlafly & Finkbeiner 2011). See Green et al. (2018) or the 3D dust mapping site for further description.

  • Samples

    For each sightline, we provide multiple estimates of the distance vs. reddening relationship. Alongside the maximum-probability density estimate (essentially, the best-fit) distance-reddening curve, we also provide samples of the distance-reddening curve, which are representative of the formal uncertainty in the relation. Most statistics you may wish to derive, like the median reddening to a given distance, are best determined by using the representative samples, rather than the best-fit relation.

  • Quality Assurance

    We include a number of pieces of information on the reliability of each pixel. A convergence flag marks whether our fit to the line-of-sight reddening curve converged. This is a formal indicator, meaning that we correctly sampled the spread of possible distance-reddening relations, given our model assumptions. It does not, however, indicate that our model assumptions were correct for that pixel. This convergence flag is based on the Gelman-Rubin diagnostic, a method of flagging Markov Chain Monte Carlo non-convergence.

    Additionally, minimum and maximum reliable distances are provided for each pixel, based on the distribution of stars along the sightline. Because we determine the presence of dust by observing differential reddening of foreground and background stars, we cannot trace dust beyond the farthest stars in a given pixel. Our estimates of dust reddening closer than the nearest observed stars in a pixel are similarly uncertain. We therefore urge caution in using our maps outside of the distance range indicated for each pixel.

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.

The two-dimensional reddening map is available as a binary FITS table, ordered according to the HEALPix scheme. The original three-dimensional dataset contains data for pixels of different sizes. The two-dimensional data have been upsampled to the minimum pixel size (HEALPix res=10, or Nside=1024). The data are set to NaN for unobserved pixels. Each record in the table has the following fields:

  • EBV
    SFD-like E(B-V) (see paper for details)

    SFD-like E(B-V) (see paper for details)

    maximum reliable distance modulus

    flag indicating whether the fit to the line of sight reddening curve converged

    the Gelman-Rubin diagnostic for the farthest distance bin, < 1.2 is good

    specifies the original pixel size (HEALPix nside) in the three-dimensional dataset

    the original nested pixel index in the three-dimensional dataset

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:

Galactic Reddening in 3D from Stellar Photometry - An Improved Map", Green, Schlafly, Finkbeiner et al. 2018, MNRAS, 478, 651. ADS

3D Dust Mapping site

Dataverse site

Back To Foreground Derivative Diffuse Components Page

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