Completeness of the Gaia-verse II: what are the odds that a star is missing from Gaia DR2?
arXiv:2005.08983 · doi:10.1093/mnras/staa2305
Abstract
The second data release of the Gaia mission contained astrometry and photometry for an incredible 1,692,919,135 sources, but how many sources did Gaia miss and where do they lie on the sky? The answer to this question will be crucial for any astronomer attempting to map the Milky Way with Gaia DR2. We infer the completeness of Gaia DR2 by exploiting the fact that it only contains sources with at least five astrometric detections. The odds that a source achieves those five detections depends on both the number of observations and the probability that an observation of that source results in a detection. We predict the number of times that each source was observed by Gaia and assume that the probability of detection is either a function of magnitude or a distribution as a function of magnitude. We fit both these models to the 1.7 billion stars of Gaia DR2, and thus are able to robustly predict the completeness of Gaia across the sky as a function of magnitude. We extend our selection function to account for crowding in dense regions of the sky, and show that this is vitally important, particularly in the Galactic bulge and the Large and Small Magellanic Clouds. We find that the magnitude limit at which Gaia is still 99% complete varies over the sky from to . We have created a new Python package selectionfunctions (https://github.com/gaiaverse/selectionfunctions) which provides easy access to our selection functions.
18 pages, re-submitted to MNRAS after the first round of comments. The Completeness of the Gaia-verse project website can be found at http://www.gaiaverse.space and the selectionfunctions Python package can be found at https://github.com/gaiaverse/selectionfunctions
References in corpus (8)
- The Gaia mission
- Gaia Data Release 1: Astrometry - one billion positions, two million proper motions and parallaxes
- Gaia Data Release 1: Catalogue validation
- emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC
- The APOGEE red-clump catalog: Precise distances, velocities, and high-resolution elemental abundances over a large area of the Milky Way's disk
- Gaia data release 1: Principles of the photometric calibration of the G band
- A Gaia early DR3 mock stellar catalog: Galactic prior and selection function
- Completeness of the Gaia-verse I: when and where were Gaia's eyes on the sky during DR2?