{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesian-cross-matching-of-high-proper-motion","title":"Bayesian Cross-Matching of High Proper Motion Stars in Gaia DR2 and Photometric Metallicities for $\\sim$1.7 million K and M Dwarfs","arxiv_id":"2102.10210","date":"2021-02-20","proceeding":null,"authors":["Ilija Medan","Sébastien Lépine","Zachary Hartman"],"abstract":"We present a Bayesian method to cross-match 5,827,988 high proper motion Gaia sources ($\\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.","url_abs":"https://arxiv.org/abs/2102.10210v1","url_pdf":"https://arxiv.org/pdf/2102.10210v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"bayesian-cross-matching-of-high-proper-motion","repo_url":"https://github.com/imedan/bayes_match","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bayesian-cross-matching-of-high-proper-motion","repo_url":"https://github.com/imedan/gpr_metallicity_relationship","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}