Chaos identification through the autocorrelation function indicator (ACFI)
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s10569-021-10036-6 http://hdl.handle.net/11449/233378 |
Resumo: | 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. |
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Repositório Institucional da UNESP |
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Chaos identification through the autocorrelation function indicator (ACFI)Asteroid beltCelestial mechanicsChaotic motionsStatistical methodsChaotic 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.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)School of Natural Sciences and Engineering São Paulo State University (UNESP)Division of Space Mechanics and Control National Space Research Institute (INPE), C.P. 515São Paulo State University (UNESP)Universidad Tecnológica del Perú (UTP), Cercado de LimaSchool of Natural Sciences and Engineering São Paulo State University (UNESP)São Paulo State University (UNESP)CNPq: 121889/2020-3FAPESP: 2016/024561-0CNPq: 301577/2017-0CAPES: 88887.374148/2019-00Universidade Estadual Paulista (UNESP)National Space Research Institute (INPE)Universidad Tecnológica del Perú (UTP)Carruba, V. [UNESP]Aljbaae, S.Domingos, R. C. [UNESP]Huaman, M.Barletta, W. [UNESP]2022-05-01T08:15:12Z2022-05-01T08:15:12Z2021-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s10569-021-10036-6Celestial Mechanics and Dynamical Astronomy, v. 133, n. 8, 2021.1572-94780923-2958http://hdl.handle.net/11449/23337810.1007/s10569-021-10036-62-s2.0-85112361312Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCelestial Mechanics and Dynamical Astronomyinfo:eu-repo/semantics/openAccess2022-05-01T08:15:12Zoai:repositorio.unesp.br:11449/233378Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-05-01T08:15:12Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Chaos identification through the autocorrelation function indicator (ACFI) |
title |
Chaos identification through the autocorrelation function indicator (ACFI) |
spellingShingle |
Chaos identification through the autocorrelation function indicator (ACFI) Carruba, V. [UNESP] Asteroid belt Celestial mechanics Chaotic motions Statistical methods |
title_short |
Chaos identification through the autocorrelation function indicator (ACFI) |
title_full |
Chaos identification through the autocorrelation function indicator (ACFI) |
title_fullStr |
Chaos identification through the autocorrelation function indicator (ACFI) |
title_full_unstemmed |
Chaos identification through the autocorrelation function indicator (ACFI) |
title_sort |
Chaos identification through the autocorrelation function indicator (ACFI) |
author |
Carruba, V. [UNESP] |
author_facet |
Carruba, V. [UNESP] Aljbaae, S. Domingos, R. C. [UNESP] Huaman, M. Barletta, W. [UNESP] |
author_role |
author |
author2 |
Aljbaae, S. Domingos, R. C. [UNESP] Huaman, M. Barletta, W. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) National Space Research Institute (INPE) Universidad Tecnológica del Perú (UTP) |
dc.contributor.author.fl_str_mv |
Carruba, V. [UNESP] Aljbaae, S. Domingos, R. C. [UNESP] Huaman, M. Barletta, W. [UNESP] |
dc.subject.por.fl_str_mv |
Asteroid belt Celestial mechanics Chaotic motions Statistical methods |
topic |
Asteroid belt Celestial mechanics Chaotic motions Statistical methods |
description |
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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-08-01 2022-05-01T08:15:12Z 2022-05-01T08:15:12Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s10569-021-10036-6 Celestial Mechanics and Dynamical Astronomy, v. 133, n. 8, 2021. 1572-9478 0923-2958 http://hdl.handle.net/11449/233378 10.1007/s10569-021-10036-6 2-s2.0-85112361312 |
url |
http://dx.doi.org/10.1007/s10569-021-10036-6 http://hdl.handle.net/11449/233378 |
identifier_str_mv |
Celestial Mechanics and Dynamical Astronomy, v. 133, n. 8, 2021. 1572-9478 0923-2958 10.1007/s10569-021-10036-6 2-s2.0-85112361312 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Celestial Mechanics and Dynamical Astronomy |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803046042775584768 |