Resumen

SOLTAU, S. B.  y  BOTTI, L. C. L.. Periodicity Detection in AGN with the Boosted Tree Method. Rev. mex. astron. astrofis [online]. 2021, vol.57, n.1, pp.107-122.  Epub 30-Sep-2021. ISSN 0185-1101.  https://doi.org/10.22201/ia.01851101p.2021.57.01.07.

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.