http://arxiv.org/abs/2102.10210
We present a Bayesian method to cross-match 5,827,988 high proper motion Gaia sources ($\mu>40 \ mas \ yr^{-1}$) to various photometric surveys: 2MASS, AllWISE, GALEX, RAVE, SDSS and Pan-STARRS. To efficiently associate these objects across catalogs, we develop a technique that compares the multidimensional distribution of all sources in the vicinity of each Gaia star to a reference distribution of random field stars obtained by extracting all sources in a region on the sky displaced 2$^\prime$. This offset preserves the local field stellar density and magnitude distribution allowing us to characterize the frequency of chance alignments. The resulting catalog with Bayesian probabilities $>$95% has a marginally higher match rate than current internal Gaia DR2 matches for most catalogs. However, a significant improvement is found with Pan-STARRS, where $\sim$99.8% of the sample within the Pan-STARRS footprint is recovered, as compared to a low $\sim$20.8% in Gaia DR2. Using these results, we train a Gaussian Process Regressor to calibrate two photometric metallicity relationships. For dwarfs of $3500<T_{eff}<5280$ K, we use metallicity values of 4,378 stars from APOGEE and Hejazi et al. (2020) to calibrate the relationship, producing results with a $1\sigma$ precision of 0.12 dex and few systematic errors. We then indirectly infer the metallicity of 4,018 stars with $2850<T_{eff}<3500$ K, that are wide companions of primaries whose metallicities are estimated with our first regressor, to produce a relationship with a $1\sigma$ precision of 0.21 dex and significant systematic errors. Additional work is needed to better remove unresolved binaries from this sample to reduce these systematic errors.
I. Medan, S. Lépine and Z. Hartman
Tue, 23 Feb 21
21/79
Comments: 51 pages, 23 figures