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 Gaia 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 Gaia 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 20 best candidates of which two 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. The 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.

翻译：第三次盖亚数据发布（DR3）包含了约170,000个位于太阳系约500秒差距范围内的双体系统天体测量轨道解算结果。确定这些系统中各组成部分的质量（特别是拥有系外行星的恒星）通常需要在天体测量数据之外结合补充观测手段，例如光谱学和径向速度测量。通过这种方式，多个包含系外行星、褐矮星、恒星及黑洞成分的盖亚DR3双体系统已得到确认。我们开发了一种替代性的机器学习方法，该方法仅使用盖亚DR3的轨道解算结果，旨在识别系外行星和褐矮星伴星的最佳候选目标。基于文献中已确认的亚恒星伴星，我们采用半监督异常检测方法，结合极端梯度提升和随机森林分类器，以确定非单星源群体中可能的低质量异常值。我们运用并研究了特征重要性，以探究该方法的合理性，并生成了一个包含20个最佳候选目标的列表，其中两个是系外行星候选体，另外五个是质量非常大的褐矮星或质量非常小的恒星。有三个候选体（包括一个初始的系外行星候选体）对应于假阳性解算结果，即用有偏的短周期轨道拟合了更长周期的双星运动。我们重点指出了九个拥有褐矮星伴星的候选目标，建议优先进行后续观测。利用外部径向速度数据，已确认类太阳恒星G\,15-6周围的伴星是一个真正的褐矮星。这种新方法是传统识别盖亚天体测量轨道中亚恒星伴星方法的有力补充，在盖亚DR4及其预期的系外行星发现成果的背景下尤为重要。