With nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates have risen significantly. Due to the surplus of astrometric data, the current computational methods employed to inspect these astrometric binary candidates are both computationally expensive and cannot be executed in a reasonable time frame. In light of this, a machine learning (ML) technique to automatically classify whether a set of stars belong to an astrometric binary pair via an artificial neural network (ANN) is proposed. Using data from Gaia DR3, the ANN was trained and tested on 1.5 million highly probable true and visual binaries, considering the proper motions, parallaxes, and angular and physical separations as features. The ANN achieves high classification scores, with an accuracy of 99.3%, a precision rate of 0.988, a recall rate of 0.991, and an AUC of 0.999, indicating that the utilized ML technique is a highly effective method for classifying astrometric binaries. Thus, the proposed ANN is a promising alternative to the existing methods for the classification of astrometric binaries.

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.

The Gaia Data Release 3 (DR3), published in June 2022, delivers a diverse set of astrometric, photometric, and spectroscopic measurements for more than a billion stars. The wealth and complexity of the data makes traditional approaches for estimating stellar parameters for the full Gaia dataset almost prohibitive. We have explored different supervised learning methods for extracting basic stellar parameters as well as distances and line-of-sight extinctions, given spectro-photo-astrometric data (including also the new Gaia XP spectra). For training we use an enhanced high-quality dataset compiled from Gaia DR3 and ground-based spectroscopic survey data covering the whole sky and all Galactic components. We show that even with a simple neural-network architecture or tree-based algorithm (and in the absence of Gaia XP spectra), we succeed in predicting competitive results (compared to Bayesian isochrone fitting) down to faint magnitudes. We will present a new Gaia DR3 stellar-parameter catalogue obtained using the currently best-performing machine-learning algorithm for tabular data, XGBoost, in the near future.