Gaia GraL: Gaia DR2 Gravitational Lens Systems. III. A systematic blind
  search for new lensed systems
  Aims: In this work, we aim to provide a reliable list of gravitational lens
(GL) candidates based on a search performed over the entire Gaia Data Release 2
(Gaia DR2). We also show that the sole astrometric and photometric informations
coming from the Gaia satellite yield sufficient insights for supervised
learning methods to automatically identify GL candidates with an efficiency
that is comparable to methods based on image processing. Methods: We simulated
106,623,188 lens systems composed of more than two images, based on a regular
grid of parameters characterizing a non-singular isothermal ellipsoid lens
model in the presence of an external shear. These simulations are used as an
input for training and testing our supervised learning models consisting of
Extremely Randomized Trees. The latter are finally used to assign to each of
the 2,129,659 clusters of celestial objects a discriminant value that reflects
the ability of our simulations to match the observed relative positions and
fluxes from each cluster. Once complemented with additional constraints, these
discriminant values allowed us to identify GL candidates out of the list of
clusters. Results: We report the discovery of 15 new quadruply-imaged lens
candidates with angular separations less than 6" and assess the performance of
our approach by recovering 12 out of the 13 known quadruply-imaged systems with
all their components detected in Gaia DR2 with a misclassification rate of
fortuitous clusters of stars as lens systems that is below one percent.
Similarly, the identification capability of our method regarding
quadruply-imaged systems where three images are detected in Gaia DR2 is
assessed by recovering 10 out of the 13 known quadruply-imaged systems having
one of their constituting images discarded. The associated misclassification
rate varying then between 5.8% and 20%, depending on the image we decided to
remove.
