Periodicity detection in AGN with the boosted tree method
  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 dataset 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 dataset and to
extract information with strategies quite different from those traditionally
used to treat time series and to obtain periodicity through the classification
of recurrent events. The results were compared with other methods from others
works that examined the same dataset and exhibit good agreement with them.
