Machine-learning identification of asteroid groups
| Autor: | Valerio Carruba, Safwan Aljbaae, A. Lucchini |
|---|---|
| Přispěvatelé: | Universidade Estadual Paulista (Unesp), Natl Space Res Inst INPE |
| Rok vydání: | 2019 |
| Předmět: |
Physics
010504 meteorology & atmospheric sciences Astronomy and Astrophysics celestial mechanics 01 natural sciences Celestial mechanics Astrobiology Space and Planetary Science Asteroid 0103 physical sciences data analysis [methods] Identification (biology) 010303 astronomy & astrophysics general [minor planets asteroids] 0105 earth and related environmental sciences |
| Zdroj: | Web of Science Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP |
| ISSN: | 1365-2966 0035-8711 |
| DOI: | 10.1093/mnras/stz1795 |
| Popis: | Made available in DSpace on 2019-10-06T07:29:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-09-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) National Aeronautics and Space Administration 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. Sao Paulo State Univ UNESP, Sch Nat Sci & Engn, BR-12516410 Guaratingueta, SP, Brazil Natl Space Res Inst INPE, Div Space Mech & Control, CP 515, BR-12227310 Sao Jose Dos Campos, SP, Brazil Sao Paulo State Univ UNESP, Sch Nat Sci & Engn, BR-12516410 Guaratingueta, SP, Brazil FAPESP: 2018/20999-6 CNPq: 301577/2017-0 |
| Databáze: | OpenAIRE |
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