Machine Learning Classification of Gaia Data Release 2
  Machine learning has increasingly gained more popularity with its incredibly
powerful ability to make predictions or calculated suggestions for large
amounts of data. We apply the machine learning classification to 85,613,922
objects in the $Gaia$ data release 2, based on the combination of the
Pan-STARRS 1 and AllWISE data. The classification results are cross-matched
with Simbad database, and the total accuracy is 91.9%. Our sample is dominated
by stars, $\sim$ 98%, and galaxies makes 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 $< \sigma_\pi/\pi <$ 0.2 could yield a very clean
stellar sample.
