Machine learning classification of new asteroid families members

Abstract

ABSTRACT 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.