@Article{SoltauBott:2021:PeDeAG,
               author = "Soltau, S. B. and Botti, Luiz Cl{\'a}udio Lima",
          affiliation = "{Universidade Federal de Alfenas (UNIFENAS)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Periodicity detection in AGN with the boosted tree method",
              journal = "Revista Mexicana de Astronom{\'{\i}}a y Astrof{\'{\i}}sica",
                 year = "2021",
               volume = "57",
               number = "1",
                pages = "107--122",
             keywords = "galaxies: active — galaxies: BL Lacertae objects: general — 
                         galaxies: quasars: general — methods: data analysis — methods: 
                         numerical.",
             abstract = "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. 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.",
                  doi = "10.22201/IA.01851101P.2021.57.01.07",
                  url = "http://dx.doi.org/10.22201/IA.01851101P.2021.57.01.07",
                 issn = "0185-1101",
             language = "en",
           targetfile = "soltau_2021.pdf",
        urlaccessdate = "20 maio 2024"
}