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.
Finding Habitable Exoplanets with Binary Relative Astrometry: Planet Detection and Characterization with the Microarcsecond Astrometric Retrieval Algorithm (MARA)
New Mexico State University（新墨西哥州立大学）；California Institute of Technology（加州理工学院）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；University of Arizona（亚利桑那大学）
A pilot study on the CSST astrometric capability: Detecting astrometric binaries with Gaia synergy via simulated data
Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；School of Astronomy and Space Sciences, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）
An algorithm to calculate the relative orbit, ephemeris, and individual masses of unresolved astrometric binaries. Example of application on the newest Gaia DR3 binaries: the ESMORGA catalog
Testing General Relativity with Binary Lens Microlensing Events
Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学前沿天文与天体物理研究所）；School of Physics and Astronomy, Beijing Normal University（北京师范大学物理学院）
The GAPS programme at TNG LXXV. Validating and confirming Gaia substellar astrometric candidates with HARPS-N
INAF – Osservatorio Astrofisico di Torino（意大利国家天体物理研究所都灵天文台）；Department of Physics, University of Rome Tor Vergata（罗马第二大学物理系）；INAF – Osservatorio Astronomico di Palermo（意大利国家天体物理研究所巴勒莫天文台）；INAF – Osservatorio Astronomico di Roma（意大利国家天体物理研究所罗马天文台）；INAF – Osservatorio Astrofisico di Catania（意大利国家天体物理研究所卡塔尼亚天文台）；INAF – Osservatorio Astronomico di Trieste（意大利国家天体物理研究所里雅斯特天文台）；Dipartimento di Fisica, Università degli Studi di Torino（都灵大学物理系）；INAF – Osservatorio Astronomico di Brera（意大利国家天体物理研究所布雷拉天文台）
Ultra-Precise Astrometric Search for Exoplanets with SKA-VLBI
International Centre for Radio Astronomy Research (ICRAR), University of Western Australia（西澳大利亚大学国际射电天文研究中心）；Joint Institute for VLBI ERIC (JIVE)（甚长基线干涉测量欧洲研究基础设施合作组织）；Leiden Observatory, Leiden University（莱顿大学莱顿天文台）
