Identifying radio active galactic nuclei with machine learning and large-area surveys

Context. Active galactic nuclei (AGNs) and star-forming galaxies (SFGs) are the primary sources in the extragalactic radio sky. But it is difficult to distinguish the radio emission produced by AGNs from that by SFGs, especially when the radio sources are faint. Best et al. (2023, MNRAS, 523, 1729) classified the radio sources in LoTSS Deep Fields DR1 through multiwavelength SED fitting. With the classification results of them, we performed a supervised machine learning to distinguish radio AGNs and radio SFGs.Aims. We aim to provide a supervised classifier to identify radio AGNs, which can get both high purity and completeness simultaneously, and can easily be applied to datasets of large-area surveys.Methods. The classifications of Best et al. (2023, MNRAS, 523, 1729) were used as the true labels for supervised machine learning. With the cross-matched sample of LoTSS Deep Fields DR1, AllWISE, and Gaia DR3, the features of optical and mid-infrared magnitude and colors were applied to train the classifier. The performance of the classifier was evaluated mainly by the precision, recall, and F_{1 score of both AGNs and non-AGNs.Results. By comparing the performance of six learning algorithms, CatBoost was chosen to construct the best classifier. The best classifier gets precision = 0.974, recall = 0.865, and F1 = 0.916 for AGNs, and precision = 0.936, recall = 0.988, and F1 = 0.961 for non-AGNs. After applying our classifier to the cross-matched sample of LoTSS DR2, AllWISE, and Gaia DR3, we obtained a sample of 49716 AGNs and 102261 non-AGNs. The reliability of these classification results was confirmed by comparing them with the spectroscopic classification of SDSS. The precision and recall of AGN sample can be as high as 94.2% and 92.3%, respectively. We also trained a model to identify radio excess sources. The F1 scores are 0.610 and 0.965 for sources with and without radio excess, respectively.}