Machine learning classification of new asteroid families members

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorDivision of Space Mechanics and Control-
Autor(es): dc.creatorCarruba, V. [UNESP]-
Autor(es): dc.creatorAljbaae, S.-
Autor(es): dc.creatorDomingos, R. C. [UNESP]-
Autor(es): dc.creatorLucchini, A. [UNESP]-
Autor(es): dc.creatorFurlaneto, P. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:35:01Z-
Data de disponibilização: dc.date.available2022-02-22T00:35:01Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-06-11-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1093/mnras/staa1463-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201970-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201970-
Descrição: dc.descriptionAsteroid 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.-
Descrição: dc.descriptionSchool of Natural Sciences and Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionNational Space Research Institute (INPE) Division of Space Mechanics and Control-
Descrição: dc.descriptionSão Paulo State University (UNESP)-
Descrição: dc.descriptionSchool of Natural Sciences and Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionSão Paulo State University (UNESP)-
Formato: dc.format540-549-
Idioma: dc.languageen-
Relação: dc.relationMonthly Notices of the Royal Astronomical Society-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcelestial mechanics-
Palavras-chave: dc.subjectminor planets, asteroids: general-
Palavras-chave: dc.subjectsoftware: data analysis-
Título: dc.titleMachine learning classification of new asteroid families members-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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