论文标题
用增压树方法在AGN中检测到周期性检测
Periodicity detection in AGN with the boosted tree method
论文作者
论文摘要
我们应用了一种称为XGBOOST的机器学习算法来探索两个无线电来源的周期性：PKS〜1921-293（OV〜236）和PKS〜2200+420（Bl〜LAC），均从Michigan University of Michigan Radio天文学观测值（Umrao），在4.8 GHZ，8.0 GHZ，WEES 14.5 ghz，以及14.5 GHZ，WE，wees，14.5 ghz，以及14.5 ghz，14.5 XGBoost提供了使用基于机器学习的方法在无线电数据集上使用基于机器学习的方法的机会，并以与传统上用于治疗时间序列的策略完全不同的策略提取信息，并通过对经常性事件的分类来获得周期性。将结果与其他作品的其他方法进行了比较，这些方法检查了相同的数据集并与它们表现出良好的一致性。
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.
