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| Natura: | Artículo científico |
| Lingua: | en |
| Pubblicazione: |
Universidad Nacional Autónoma de México
2021
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| Accesso online: | https://www.redalyc.org/articulo.oa?id=57171634007 https://www.redalyc.org/journal/571/57171634007/ https://www.redalyc.org/journal/571/57171634007/html/ https://www.redalyc.org/journal/571/57171634007/57171634007.epub https://www.redalyc.org/journal/571/57171634007/movil |
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Sommario:
- Periodicity Detection in AGN with the Boosted Tree Method S. B. Soltau L. C. L. Botti Física, Astronomía y Matemáticas Galaxies: active methods: numerical methods: data analysis galaxies: quasars: general galaxies: BL Lacertae objects: general We 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. 2021 artículo científico 0185-1101 https://www.redalyc.org/articulo.oa?id=57171634007 https://www.redalyc.org/journal/571/57171634007/ https://www.redalyc.org/journal/571/57171634007/html/ https://www.redalyc.org/journal/571/57171634007/57171634007.epub https://www.redalyc.org/journal/571/57171634007/movil 10.22201/ia.01851101p.2021.57.01.07 en http://www.redalyc.org/revista.oa?id=571 Revista Mexicana de Astronomía y Astrofísica application/pdf Universidad Nacional Autónoma de México Revista Mexicana de Astronomía y Astrofísica (México) Num.1 Vol.57