Hierarchical probabilistic inference of the color-magnitude diagram and shrinkage of stellar distance uncertainties

Understand

We present a hierarchical probabilistic model for improving geometric stellar distance estimates using color--magnitude information.

• This is achieved with a data driven model of the color--magnitude diagram, not relying on stellar models but instead on the relative abundances of stars in color--magnitude cells, which are inferred from very noisy magnitudes and parallaxes.
• While the resulting noise-deconvolved color--magnitude diagram can be useful for a range of applications, we focus on deriving improved stellar distance estimates relying on both parallax and photometric information.
• We demonstrate the efficiency of this approach on the 1.4 million stars of the Gaia TGAS sample that also have APASS magnitudes.