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Identification of asteroid groups in the z1 and z2 nonlinear secular resonances through genetic algorithms

Authors :
R. C. Domingos
Safwan Aljbaae
Valerio Carruba
Universidade Estadual Paulista (Unesp)
National Space Research Institute (INPE)
Source :
Scopus, Repositório Institucional da UNESP, Universidade Estadual Paulista (UNESP), instacron:UNESP
Publication Year :
2021

Abstract

Made available in DSpace on 2021-06-25T11:16:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-06-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Linear secular resonances are observed when there is a ratio between the precession period of the longitudes of pericenter or nodes of a minor body and a planet. Nonlinear secular resonances occur for higher-order combinations of frequencies. They can change the shape of asteroid families in the (a, e, sin (i)) proper elements space. Identifying asteroids in secular resonances requires performing numerical simulations, and then visually inspecting if the resonant argument is librating, which is generally a time-consuming procedure. Here, we use machine learning genetic algorithms to select the most optimal model and training set to best-fit asteroids likely to be in librating states of the z1 and z2 secular resonances. We then identify groups in domains of librating asteroids, as predicted by our algorithms, and verified whether these clusters belong to known collisional families. Using this approach, we retrieved all the asteroid families known to interact with the two resonances and identified 5 fairly robust previously unknown groups. School of Natural Sciences and Engineering São Paulo State University (UNESP) Division of Space Mechanics and Control National Space Research Institute (INPE), C.P. 515 São Paulo State University (UNESP) School of Natural Sciences and Engineering São Paulo State University (UNESP) São Paulo State University (UNESP) FAPESP: 2018/20999-6 CNPq: 30157/2017-0 CAPES: 88887.374148/2019-00

Details

Language :
English
Database :
OpenAIRE
Journal :
Scopus, Repositório Institucional da UNESP, Universidade Estadual Paulista (UNESP), instacron:UNESP
Accession number :
edsair.doi.dedup.....2f5449271bec6c1e48336d30504b9385