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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/44MMSGL
Repositorysid.inpe.br/mtc-m21c/2021/05.17.14.54
Last Update2021:05.17.14.54.03 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2021/05.17.14.54.03
Metadata Last Update2022:04.03.19.24.43 (UTC) administrator
DOI10.22201/IA.01851101P.2021.57.01.07
ISSN0185-1101
Citation KeySoltauBott:2021:PeDeAG
TitlePeriodicity detection in AGN with the boosted tree method
Year2021
Access Date2024, May 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size5350 KiB
2. Context
Author1 Soltau, S. B.
2 Botti, Luiz Cláudio Lima
Resume Identifier1
2 8JMKD3MGP5W/3C9JHN7
ORCID1 0000-0002-7211-2533
2 0000-0003-1424-0796
Group1
2 DIAST-CGCE-INPE-MCTI-GOV-BR
Affiliation1 Universidade Federal de Alfenas (UNIFENAS)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2 luizquas@yahoo.com.br
JournalRevista Mexicana de Astronomía y Astrofísica
Volume57
Number1
Pages107-122
Secondary MarkB1_ASTRONOMIA_/_FÍSICA
History (UTC)2021-05-17 14:54:03 :: simone -> administrator ::
2021-05-17 14:54:05 :: administrator -> simone :: 2021
2021-05-17 14:54:46 :: simone -> administrator :: 2021
2022-04-03 19:24:43 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsgalaxies: active — galaxies: BL Lacertae objects: general — galaxies: quasars: general — methods: data analysis — methods: numerical
AbstractWe apply a machine learning algorithm called XGBoost to explore the periodicity of two radio sources: PKS 1921-293 (OV 236) and PKS 2200+420 (BL Lac), both radio frequency datasets obtained from University of Michigan Radio Astronomy Observatory (UMRAO), at 4.8 GHz, 8.0 GHz, and 14.5 GHz, between 1969 to 2012. From this methods, we find that the XGBoost provides the opportunity to use a machine learning based methodology on radio datasets and to extract information with strategies quite different from those traditionally used to treat time series, as well as to obtain periodicity through the classification of recurrent events. The results were compared with other methods that examined the same datasets and exhibit a good agreement with them. RESUMEN: Aplicamos un algoritmo de aprendizaje autom´atico llamado XGBoost para explorar la periodicidad de dos fuentes de radio: PKS 1921-293 (OV 236) y PKS 2200+420 (BL Lac), ambos conjuntos de datos de radiofrecuencia obtenidos del Observatorio de Radio Astronom´ıa de la Universidad de Michigan (UMRAO), a 4.8 GHz, 8.0 GHz, y 14.5 GHz, entre 1969 y 2012. A partir de estos m´etodos, encontramos que XGBoost brinda la oportunidad de utilizar una metodolog´ıa basada en aprendizaje autom´atico en el conjunto de datos de radio y extraer informaci´on con estrategias bastante diferentes de las utilizadas tradicionalmente para tratar series temporales y obtener periodicidad a trav´es de la clasificaci´on de eventos recurrentes. Los resultados se compararon con los obtenidos en otros trabajos que examinaron el mismo conjunto de datos y muestraron resultados compatibles.
AreaCEA
Arrangementurlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCE > Periodicity detection in...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://mtc-m21c.sid.inpe.br/ibi/8JMKD3MGP3W34R/44MMSGL
zipped data URLhttp://mtc-m21c.sid.inpe.br/zip/8JMKD3MGP3W34R/44MMSGL
Languageen
Target Filesoltau_2021.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KTFK8
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.54.14 1
DisseminationWEBSCI; PORTALCAPES; SCIELO; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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