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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Machine-learning identification of asteroid groups

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Author(s):
Carruba, V. [1] ; Aljbaae, S. [2] ; Lucchini, A. [1]
Total Authors: 3
Affiliation:
[1] Sao Paulo State Univ UNESP, Sch Nat Sci & Engn, BR-12516410 Guaratingueta, SP - Brazil
[2] Natl Space Res Inst INPE, Div Space Mech & Control, CP 515, BR-12227310 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Monthly Notices of the Royal Astronomical Society; v. 488, n. 1, p. 1377-1386, SEP 2019.
Web of Science Citations: 0
Abstract

Asteroid families are groups of asteroids that share a common origin. They can be the outcome of a collision or be the result of the rotational failure of a parent body or its satellites. Collisional asteroid families have been identified for several decades using hierarchical clustering methods (HCMs) in proper elements domains. In this method, the distance of an asteroid from a reference body is computed, and, if it is less than a critical value, the asteroid is added to the family list. The process is then repeated with the new object as a reference, until no new family members are found. Recently, new machine-learning clustering algorithms have been introduced for the purpose of cluster classification. Here, we apply supervised-learning hierarchical clustering algorithms for the purpose of asteroid families identification. The accuracy, precision, and recall values of results obtained with the new method, when compared with classical HCM, show that this approach is able to found family members with an accuracy above 89.5 per cent, and that all asteroid previously identified as family members by traditional methods are consistently retrieved. Values of the areas under the curve coefficients below Receiver Operating Characteristic curves are also optimal, with values consistently above 85 per cent. Overall, we identify 6 new families and 13 new clumps in regions where the method can be applied that appear to be consistent and homogeneous in terms of physical and taxonomic properties. Machine-learning clustering algorithms can, therefore, be very efficient and fast tools for the problem of asteroid family identification. (AU)

FAPESP's process: 18/20999-6 - Young asteroid families: exploring the limit between fission groups and collisional families
Grantee:Valerio Carruba
Support type: Regular Research Grants