Machine-learning identification of asteroid groups
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1093/mnras/stz1795 http://hdl.handle.net/11449/186832 |
Resumo: | 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. |
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Repositório Institucional da UNESP |
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Machine-learning identification of asteroid groupsmethods: data analysiscelestial mechanicsminor planets, asteroids: generalAsteroid 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)National Aeronautics and Space AdministrationSao Paulo State Univ UNESP, Sch Nat Sci & Engn, BR-12516410 Guaratingueta, SP, BrazilNatl Space Res Inst INPE, Div Space Mech & Control, CP 515, BR-12227310 Sao Jose Dos Campos, SP, BrazilSao Paulo State Univ UNESP, Sch Nat Sci & Engn, BR-12516410 Guaratingueta, SP, BrazilFAPESP: 2018/20999-6CNPq: 301577/2017-0Oxford Univ PressUniversidade Estadual Paulista (Unesp)Natl Space Res Inst INPECarruba, V. [UNESP]Aljbaae, S.Lucchini, A. [UNESP]2019-10-06T07:29:40Z2019-10-06T07:29:40Z2019-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1377-1386http://dx.doi.org/10.1093/mnras/stz1795Monthly Notices Of The Royal Astronomical Society. Oxford: Oxford Univ Press, v. 488, n. 1, p. 1377-1386, 2019.0035-8711http://hdl.handle.net/11449/18683210.1093/mnras/stz1795WOS:000482319700100Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMonthly Notices Of The Royal Astronomical Societyinfo:eu-repo/semantics/openAccess2024-07-02T14:28:56Zoai:repositorio.unesp.br:11449/186832Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-07-02T14:28:56Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine-learning identification of asteroid groups |
title |
Machine-learning identification of asteroid groups |
spellingShingle |
Machine-learning identification of asteroid groups Carruba, V. [UNESP] methods: data analysis celestial mechanics minor planets, asteroids: general |
title_short |
Machine-learning identification of asteroid groups |
title_full |
Machine-learning identification of asteroid groups |
title_fullStr |
Machine-learning identification of asteroid groups |
title_full_unstemmed |
Machine-learning identification of asteroid groups |
title_sort |
Machine-learning identification of asteroid groups |
author |
Carruba, V. [UNESP] |
author_facet |
Carruba, V. [UNESP] Aljbaae, S. Lucchini, A. [UNESP] |
author_role |
author |
author2 |
Aljbaae, S. Lucchini, A. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Natl Space Res Inst INPE |
dc.contributor.author.fl_str_mv |
Carruba, V. [UNESP] Aljbaae, S. Lucchini, A. [UNESP] |
dc.subject.por.fl_str_mv |
methods: data analysis celestial mechanics minor planets, asteroids: general |
topic |
methods: data analysis celestial mechanics minor planets, asteroids: general |
description |
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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T07:29:40Z 2019-10-06T07:29:40Z 2019-09-01 |
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/stz1795 Monthly Notices Of The Royal Astronomical Society. Oxford: Oxford Univ Press, v. 488, n. 1, p. 1377-1386, 2019. 0035-8711 http://hdl.handle.net/11449/186832 10.1093/mnras/stz1795 WOS:000482319700100 |
url |
http://dx.doi.org/10.1093/mnras/stz1795 http://hdl.handle.net/11449/186832 |
identifier_str_mv |
Monthly Notices Of The Royal Astronomical Society. Oxford: Oxford Univ Press, v. 488, n. 1, p. 1377-1386, 2019. 0035-8711 10.1093/mnras/stz1795 WOS:000482319700100 |
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 |
1377-1386 |
dc.publisher.none.fl_str_mv |
Oxford Univ Press |
publisher.none.fl_str_mv |
Oxford Univ Press |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1803649643145330688 |