Machine learning classification of new asteroid families members
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1093/mnras/staa1463 http://hdl.handle.net/11449/201970 |
Resumo: | 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. |
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Repositório Institucional da UNESP |
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2946 |
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Machine learning classification of new asteroid families memberscelestial mechanicsminor planets, asteroids: generalsoftware: data analysisAsteroid 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 ControlSão Paulo State University (UNESP)School of Natural Sciences and Engineering São Paulo State University (UNESP)São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)Division of Space Mechanics and ControlCarruba, V. [UNESP]Aljbaae, S.Domingos, R. C. [UNESP]Lucchini, A. [UNESP]Furlaneto, P. [UNESP]2020-12-12T02:46:31Z2020-12-12T02:46:31Z2020-06-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article540-549http://dx.doi.org/10.1093/mnras/staa1463Monthly Notices of the Royal Astronomical Society, v. 496, n. 1, p. 540-549, 2020.1365-29660035-8711http://hdl.handle.net/11449/20197010.1093/mnras/staa14632-s2.0-8508857742566521690834643270000-0002-0516-0420Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMonthly Notices of the Royal Astronomical Societyinfo:eu-repo/semantics/openAccess2024-07-02T14:28:50Zoai:repositorio.unesp.br:11449/201970Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-07-02T14:28:50Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine learning classification of new asteroid families members |
title |
Machine learning classification of new asteroid families members |
spellingShingle |
Machine learning classification of new asteroid families members Carruba, V. [UNESP] celestial mechanics minor planets, asteroids: general software: data analysis |
title_short |
Machine learning classification of new asteroid families members |
title_full |
Machine learning classification of new asteroid families members |
title_fullStr |
Machine learning classification of new asteroid families members |
title_full_unstemmed |
Machine learning classification of new asteroid families members |
title_sort |
Machine learning classification of new asteroid families members |
author |
Carruba, V. [UNESP] |
author_facet |
Carruba, V. [UNESP] Aljbaae, S. Domingos, R. C. [UNESP] Lucchini, A. [UNESP] Furlaneto, P. [UNESP] |
author_role |
author |
author2 |
Aljbaae, S. Domingos, R. C. [UNESP] Lucchini, A. [UNESP] Furlaneto, P. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Division of Space Mechanics and Control |
dc.contributor.author.fl_str_mv |
Carruba, V. [UNESP] Aljbaae, S. Domingos, R. C. [UNESP] Lucchini, A. [UNESP] Furlaneto, P. [UNESP] |
dc.subject.por.fl_str_mv |
celestial mechanics minor planets, asteroids: general software: data analysis |
topic |
celestial mechanics minor planets, asteroids: general software: data analysis |
description |
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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:46:31Z 2020-12-12T02:46:31Z 2020-06-11 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1093/mnras/staa1463 Monthly Notices of the Royal Astronomical Society, v. 496, n. 1, p. 540-549, 2020. 1365-2966 0035-8711 http://hdl.handle.net/11449/201970 10.1093/mnras/staa1463 2-s2.0-85088577425 6652169083464327 0000-0002-0516-0420 |
url |
http://dx.doi.org/10.1093/mnras/staa1463 http://hdl.handle.net/11449/201970 |
identifier_str_mv |
Monthly Notices of the Royal Astronomical Society, v. 496, n. 1, p. 540-549, 2020. 1365-2966 0035-8711 10.1093/mnras/staa1463 2-s2.0-85088577425 6652169083464327 0000-0002-0516-0420 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Monthly Notices of the Royal Astronomical Society |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
540-549 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803649314131542016 |