Machine learning classification of Gaia Data Release 2

Yu Bai, Ji-Feng Liu, Song Wang


Machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculate suggestions for large amounts of data. We apply machine learning classification to 85 613 922 objects in the Gaia Data Release 2, based on a combination of Pan-STARRS 1 and AllWISE data. The classification results are cross-matched with the Simbad database, and the total accuracy is 91.9%. Our sample is dominated by stars, ∼98%, and galaxies make up 2%. For the objects with negative parallaxes, about 2.5% are galaxies and QSOs, while about 99.9% are stars if the relative parallax uncertainties are smaller than 0.2. Our result implies that using the threshold of 0 < σπ/π < 0.2 could yield a very clean stellar sample.


methods: data analysis — stars: general — Gaia catalog https://doi.org/10.1088/1674–4527/18/10/118

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