Machine learning-based identification of Gaia astrometric exoplanet
  orbits
Johannes Sahlmann Pablo Gómez
The third Gaia data release (DR3) contains $\sim$170 000 astrometric orbit
solutions of two-body systems located within $\sim$500 pc of the Sun.
Determining component masses in these systems, in particular of stars hosting
exoplanets, usually hinges on incorporating complementary observations in
addition to the astrometry, e.g. spectroscopy and radial velocities. Several
DR3 two-body systems with exoplanet, brown-dwarf, stellar, and black-hole
components have been confirmed in this way. We developed an alternative machine
learning approach that uses only the DR3 orbital solutions with the aim of
identifying the best candidates for exoplanets and brown-dwarf companions.
Based on confirmed substellar companions in the literature, we use
semi-supervised anomaly detection methods in combination with extreme gradient
boosting and random forest classifiers to determine likely low-mass outliers in
the population of non-single sources. We employ and study feature importance to
investigate the method's plausibility and produced a list of 22 best candidates
of which four are exoplanet candidates and another five are either very-massive
brown dwarfs or very-low mass stars. Three candidates, including one initial
exoplanet candidate, correspond to false-positive solutions where longer-period
binary star motion was fitted with a biased shorter-period orbit. We highlight
nine candidates with brown-dwarf companions for preferential follow-up. One
candidate companion around the Sun-like star G 15-6 could be confirmed as a
genuine brown dwarf using external radial-velocity data. This new approach is a
powerful complement to the traditional identification methods for substellar
companions among Gaia astrometric orbits. It is particularly relevant in the
context of Gaia DR4 and its expected exoplanet discovery yield.