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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Sitemtc-m21b.sid.inpe.br (namespace prefix: upn:3ET5SFU)
Identificador8JMKD3MGP3W34P/3PP6C52
Repositóriosid.inpe.br/mtc-m21b/2017/10.05.18.45
Última Atualização2017:10.05.18.45.25 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m21b/2017/10.05.18.45.25
Última Atualização dos Metadados2021:07.28.23.02.13 (UTC) administrator
DOI10.1051/0004-6361/201630109
ISSN0004-6361
1432-0746
Chave de CitaçãoLopesCros:2017:IIOb
TítuloNew insights into time series analysis: II-Non-correlated observations
Ano2017
MêsAug.
Data de Acesso21 set. 2026
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho2442 KiB
2. Contextualização
Autor1 Lopes, Carlos Eduardo Ferreira
2 Cross, N. J. G.
Grupo1 CGCEA-CGCEA-INPE-MCTIC-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Scottish Universities Physics Alliance (SUPA)
Endereço de e-Mail do Autor1 carlos.lopes@inpe.br
RevistaAstronomy & Astrophysics
Volume604
PáginasA121
Nota SecundáriaA1_GEOCIÊNCIAS A1_ENGENHARIAS_III A2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA A2_INTERDISCIPLINAR A2_ENGENHARIAS_IV A2_ASTRONOMIA_/_FÍSICA B2_ENSINO C_ENGENHARIAS_II
Acervo Hospedeirosid.inpe.br/mtc-m21b/2013/09.26.14.25.20 upn:3ET5SFU
Histórico (UTC)2017-10-05 18:45:25 :: simone -> administrator ::
2017-10-05 18:45:25 :: administrator -> simone :: 2017
2017-10-05 18:45:57 :: simone -> administrator :: 2017
2021-07-28 23:02:13 :: administrator -> simone :: 2017
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-Chavemethods: data analysis – methods: statistical – techniques: photometric – astronomical databases: miscellaneous – stars: variables: general – infrared: general
ResumoStatistical parameters are used to draw conclusions in a vast number of fields such as finance, weather, industrial, and science. These parameters are also used to identify variability patterns on photometric data to select non-stochastic variations that are indicative of astrophysical effects. New, more efficient, selection methods are mandatory to analyze the huge amount of astronomical data. Aims. We seek to improve the current methods used to select non-stochastic variations on non-correlated data. Methods. We used standard and new data-mining parameters to analyze non-correlated data to find the best way to discriminate between stochastic and non-stochastic variations. A new approach that includes a modified Strateva function was performed to select non-stochastic variations. Monte Carlo simulations and public time-domain data were used to estimate its accuracy and performance. Results. We introduce 16 modified statistical parameters covering different features of statistical distribution such as average, dispersion, and shape parameters. Many dispersion and shape parameters are unbound parameters, i.e. equations that do not require the calculation of average. Unbound parameters are computed with single loop and hence decreasing running time. Moreover, the majority of these parameters have lower errors than previous parameters, which is mainly observed for distributions with few measurements. A set of non-correlated variability indices, sample size corrections, and a new noise model along with tests of different apertures and cut-offs on the data (BAS approach) are introduced. The number of mis-selections are reduced by about 520% using a single waveband and 1200% combining all wavebands. On the other hand, the even-mean also improves the correlated indices introduced in Paper I. The mis-selection rate is reduced by about 18% if the even-mean is used instead of the mean to compute the correlated indices in the WFCAM database. Even-statistics allows us to improve the effectiveness of both correlated and non-correlated indices. Conclusions. The selection of non-stochastic variations is improved by non-correlated indices. The even-Averages provide a better estimation of mean and median for almost all statistical distributions analyzed. The correlated variability indices, which are proposed in the first paper of this series, are also improved if the even-mean is used. The even-parameters will also be useful for classifying light curves in the last step of this project. We consider that the first step of this project, where we set new techniques and methods that provide a huge improvement on the efficiency of selection of variable stars, is now complete. Many of these techniques may be useful for a large number of fields. Next, we will commence a new step of this project regarding the analysis of period search methods.
ÁreaCEA
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4. Condições de acesso e uso
URI dos dadosupn:3ET5SFU:8JMKD3MGP3W34P/3PP6C52
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGP3W34P/3PP6C52
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP3W34P/3PP6C52
Idiomaen
Arquivo Alvolopes_new.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentoallowpublisher allowfinaldraft
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3EU2FR5
Lista de Itens Citandosid.inpe.br/bibdigital/2013/10.01.23.29 - 138
sid.inpe.br/bibdigital@80/2006/04.07.15.50.13 - 2
DivulgaçãoWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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