Identification of asteroid families' members

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidad tecnológica del Perú (UTP)-
Autor(es): dc.creatorDomingos, R. C.-
Autor(es): dc.creatorHuaman, M.-
Autor(es): dc.creatorLourenço, M. V.F.-
Data de aceite: dc.date.accessioned2025-08-21T18:23:34Z-
Data de disponibilização: dc.date.available2025-08-21T18:23:34Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/B978-0-44-324770-5.00007-6-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308573-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308573-
Descrição: dc.descriptionAsteroid families are groupings of objects with a common origin (parent body) generated by collision events, rotational fission of the parent body, characteristic ejection velocities, and a consequence of the dynamic region where they survive. The hierarchical clustering approach identifies these groupings in proper elements or frequency domains. However, HCM needs to improve accuracy in regions of high population density, where it is almost impossible to differentiate members between neighboring families. The gradual increase in large, reliable databases of asteroid proper elements has generated the need to use more sophisticated algorithms, such as machine learning or genetic algorithms. This chapter reviews supervised, unsupervised, and genetic algorithms that classify new asteroid family members. The best free hyperparameters (FP) were compared to determine the most effective algorithm. In comparison, genetic algorithms were observed as a more optimal tool; an efficient and faster alternative was obtained by obtaining more optimal hyperparameters.-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Engineering Department of Electronic and Telecommunications Engineering, SP-
Descrição: dc.descriptionUniversidad tecnológica del Perú (UTP)-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Engineering and Sciences Department of Mathematics, SP-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Engineering Department of Electronic and Telecommunications Engineering, SP-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Engineering and Sciences Department of Mathematics, SP-
Formato: dc.format33-57-
Idioma: dc.languageen-
Relação: dc.relationMachine Learning for Small Bodies in the Solar System-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAsteroids: general-
Palavras-chave: dc.subjectCelestial mechanics-
Palavras-chave: dc.subjectGenetic algorithms-
Palavras-chave: dc.subjectMachine learning methods-
Palavras-chave: dc.subjectMinor planets-
Título: dc.titleIdentification of asteroid families' members-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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