3D dust map from Green et al. (2019)

A three-dimensional map of dust reddening, covering the three quarters of the sky surveyed by the Pan-STARRS 1 (PS1) survey (declination δ&nbsp>&nbsp-30°). We use PS1 and 2MASS optical and near-infrared photometry, in conjunction with Gaia DR2 parallaxes, to infer distances and reddenings to ~800 million stars. These stars trace the reddening along different lines of sight, allowing us to build up a map of reddening in 3D. This work builds on Green et al. (2014, 2015, 2017). The map is structured as a set of sightlines, each of which contains multiple samples of the cumulative dust reddening as a function of distance. Each sightline is identified by a HEALPix nside parameter and nested pixel index. Within each sightline, cumulative reddening is given at discrete distances, spaced evenly in distance modulus. For each pixel, we provide multiple samples from the posterior on dust reddening. Quality assurance information is given for each pixel, including: Whether the fit converged in the pixel The minimum/maximum reliable distance moduli in the pixel The number of stars in the sightline The number of stars in the sightline which pass a cut on maximum-likelihood χ2/passband (termed "good" stars) The number of "good" stars which are inferred to be Main-Sequence stars (defined as Mr&nbsp Reddening is given in an arbitrary unit, which can be converted to extinction in the PS1 and 2MASS passbands by multiplying by the following coefficients: gP1: 3.518 rP1: 2.617 iP1: 1.971 zP1: 1.549 yP1: 1.263 J: 0.7927 H: 0.4690 Ks: 0.3026 The 3D map is described in more detail in Green et al. (2019), and tools for accessing the map are provided at argonaut.skymaps.info. This map is included in the Python dustmaps package, which is available through pip: pip install dustmaps