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

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,...

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Detalles Bibliográficos
Autores: Carruba, V. [UNESP], Aljbaae, S., Domingos, R. C. [UNESP]
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:Brasil
Institución:Universidade Estadual Paulista (UNESP)
Repositorio:Repositório Institucional da UNESP
Idioma:inglés
OAI Identifier:oai:repositorio.unesp.br:11449/208687
Acceso en línea:http://dx.doi.org/10.1007/s10569-021-10021-z
http://hdl.handle.net/11449/208687
Access Level:acceso abierto
Palabra clave:Asteroids: general
Asteroids: individual
Celestial mechanics
Data analysis
Methods
Minor planets
Descripción
Sumario: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.