Chaotic dynamics

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorDivision of Graduate Studies-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorCaritá, Gabriel-
Autor(es): dc.creatorAlves, Abreuçon Atanasio-
Autor(es): dc.creatorCarruba, Valerio-
Data de aceite: dc.date.accessioned2025-08-21T17:01:18Z-
Data de disponibilização: dc.date.available2025-08-21T17:01:18Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/B978-0-44-324770-5.00015-5-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309251-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309251-
Descrição: dc.descriptionThe chaotic movement of small celestial bodies within the Solar System may result from factors such as close encounters, collisions, or resonance overlapping. Various methods can be employed to identify chaotic motion, including those that gauge the separation rate of trajectories starting infinitesimally close or assess frequencies of time series. In this chapter, we illustrate a novel approach utilizing the autocorrelation function of time series, referred to as the ACF index (ACFI). Autocorrelation coefficients measure the correlation between a time series and a lagged copy of itself. By evaluating the fraction of autocorrelation coefficients exceeding the 5% null hypothesis threshold after a specific time lag, we can ascertain how well the time series autocorrelates with itself. This aids in pinpointing unpredictable time series, characterized by low ACFI values. When applied to orbital regions affected by both resonance overlapping and chaos induced by close encounters, ACFI demonstrates effectiveness in correctly identifying motion stemming from resonance overlapping, albeit showing limited sensitivity to chaos induced by close encounters. We apply this to the Henon-Heiles system, asteroids families, and to the circular restricted three body problem. ACFI holds potential for discerning chaotic effects acting as global dynamical thermometer and a tool for pre-processing data for further time series analysis using machine learning.-
Descrição: dc.descriptionNational Institute for Space and Research (INPE) Division of Graduate Studies, SP-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Mathematics, SP-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Mathematics, SP-
Formato: dc.format273-293-
Idioma: dc.languageen-
Relação: dc.relationMachine Learning for Small Bodies in the Solar System-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAsteroids-
Palavras-chave: dc.subjectCelestial mechanics-
Palavras-chave: dc.subjectChaos-
Palavras-chave: dc.subjectStatistical methods-
Título: dc.titleChaotic dynamics-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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