114 A Catalog of Nearby Accelerating Star Candidates in Gaia DR3

Faculty Mentor: Ben Bromley (Physics & Astronomy, University of Utah)

 We present a new compilation of accelerating star candidates, known as the Gaia Nearby Accelerating Star Catalog (GNASC), featuring 29,684 candidates within 100 pc and brighter than Gaia G 17.5 mag. This catalog was developed using a supervised machine learning algorithm, drawing data from the Hipparcos-Gaia Catalog of Accelerations (HGCA), Gaia Data Release 2, and Gaia Early Data Release 3. Our method exploits the difference in observation periods between Gaia catalogs and astrometric modeling quality, assuming a correlation with acceleration. 

Membership in the catalog is determined by confidently ruling out constant proper motion over 30 years (greater than 99.9% confidence). According to test data, each catalog member has a 68% chance of actual astrometric acceleration, with some subsets exceeding 85%. We compared GNASC to Gaia Data Release 3, capturing over 96% of sources that fit our criteria in its high-confidence acceleration table. 

Our catalog includes bright, nearby candidates not present in the initial Hipparcos survey, comprising members of known binary systems and stars with yet-to-be-identified companions. GNASC extends the HGCA and highlights the potential of machine learning in uncovering hidden stellar partners in future astrometric surveys. 

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