Keywords
galaxies: active
galaxies: BL Lacertae objects: general
galaxies: quasars: general
methods: data analysis
methods: numerical
Astrophysics - Astrophysics of Galaxies
Physics - Computational Physics
galaxies: BL Lacertae objects: general
galaxies: quasars: general
methods: data analysis
methods: numerical
Astrophysics - Astrophysics of Galaxies
Physics - Computational Physics
How to Cite
Periodicity Detection in AGN with the Boosted Tree Method. (2021). Revista Mexicana De Astronomía Y Astrofísica, 57(1), 107-122. https://doi.org/10.22201/ia.01851101p.2021.57.01.07
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.Similar Articles
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