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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Identification of asteroid groups in the z(1) and z(2) nonlinear secular resonances through genetic algorithms

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Author(s):
Carruba, V [1] ; Aljbaae, S. [2] ; Domingos, R. C. [3]
Total Authors: 3
Affiliation:
[1] Sao Paulo State Univ UNESP, Sch Nat Sci & Engn, BR-12516410 Guaratingueta, SP - Brazil
[2] Natl Space Res Inst INPE, Div Space Mech & Control, BR-12227310 Sao Jose Dos Campos, SP - Brazil
[3] Sao Paulo State Univ UNESP, BR-13874149 Sao Joao Da Boa Vista, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: CELESTIAL MECHANICS & DYNAMICAL ASTRONOMY; v. 133, n. 6 JUN 2021.
Web of Science Citations: 0
Abstract

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 z(1) and z(2) 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. (AU)

FAPESP's process: 18/20999-6 - Young asteroid families: exploring the limit between fission groups and collisional families
Grantee:Valerio Carruba
Support type: Regular Research Grants