Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object. These groups are mainly identified in proper elements or frequencies domains. Because of robotic telescope surveys, the number of known asteroids has increased from |${\simeq}10\, 000$| in the early 1990s to more than |$750\, 000$| nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a, e, sin (i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand-alone and ensemble approaches. The extremely randomized trees (ExtraTree) method had the highest precision, enabling to retrieve up to 97 per cent of family members identified with standard HCM.

中文翻译：

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新的小行星族成员的机器学习分类

小行星族是小行星的组，它们是碰撞或母体旋转裂变的产物。这些组主要在适当的元素或频域中标识。由于进行了自动望远镜勘测，已知小行星的数量从| $ {\ simeq} 10 \，000 $ | 在1990年代初超过$ 750 \，000 $ | 如今。识别小行星家族新成员的传统方法，例如层次聚类方法（HCM），可能难以跟上新发现的增长速度。在这里，我们使用机器学习分类算法，根据适当的（a，e，sin（i））以前已知的家庭成员。我们比较了来自独立方法和集成方法的九种分类算法的结果。极随机树（ExtraTree）方法具有最高的精确度，可以检索到多达97％的通过标准HCM识别的家庭成员。