Chaos identification through the autocorrelation function indicator (ACFI)
| Autor: | Safwan Aljbaae, M. Huaman, Valerio Carruba, W. Barletta, R. C. Domingos |
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| Přispěvatelé: | Universidade Estadual Paulista (UNESP), National Space Research Institute (INPE), Universidad Tecnológica del Perú (UTP) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Physics
Series (mathematics) Statistical methods Applied Mathematics Autocorrelation Mathematical analysis Chaotic Motion (geometry) Spectral density Astronomy and Astrophysics Chaotic motions Measure (mathematics) Resonance (particle physics) Asteroid belt Computational Mathematics Space and Planetary Science Modeling and Simulation Celestial mechanics Fraction (mathematics) Mathematical Physics |
| Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP |
| Popis: | Made available in DSpace on 2022-05-01T08:15:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-08-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Chaotic motion affecting small bodies in the Solar system can be caused by close encounters or collisions or by resonance overlapping. Chaotic motion can be detected using approaches that measure the separation rate of trajectories that starts infinitesimally close or changes in the frequency power spectrum of time series, among others. In this work, we introduce an approach based on the autocorrelation function of time series, the ACF index (ACFI). Autocorrelation coefficients measure the correlation of a time series with a lagged copy of itself. By measuring the fraction of autocorrelation coefficients obtained after a given time lag that are higher than the 5% null hypothesis threshold, we can determine how the time series autocorrelates with itself. This allows identifying unpredictable time series, characterized by low values of ACFI. Applications of ACFI to orbital regions affected by both types of chaos show that this method can correctly identify chaotic motion caused by resonance overlapping, but it is mostly blind to close encounters induced chaos. ACFI could be used in these regions to select the effects of resonance overlapping. 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) Universidad Tecnológica del Perú (UTP), Cercado de Lima School of Natural Sciences and Engineering São Paulo State University (UNESP) São Paulo State University (UNESP) CNPq: 121889/2020-3 FAPESP: 2016/024561-0 CNPq: 301577/2017-0 CAPES: 88887.374148/2019-00 |
| Databáze: | OpenAIRE |
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