Machine learning classification of new asteroid families members

Made available in DSpace on 2020-12-12T02:46:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-11 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 ∼eq10000 in the early 1990s to more than 750000 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. School of Natural Sciences and Engineering São Paulo State University (UNESP) National Space Research Institute (INPE) Division of Space Mechanics and Control São Paulo State University (UNESP) School of Natural Sciences and Engineering São Paulo State University (UNESP) São Paulo State University (UNESP)