Identifying Radio Active Galactic Nuclei with Machine Learning and Large-Area Surveys

TL;DR

This paper develops a supervised machine learning classifier, using optical and mid-infrared features, to distinguish radio AGNs from star-forming galaxies in large-area surveys with high accuracy and reliability.

Contribution

It introduces a high-performance classifier trained on multiwavelength data, achieving high precision and recall, and applies it to large survey datasets for reliable AGN identification.

Findings

The CatBoost classifier achieved precision of 97.4% for AGNs.

The classifier's recall for AGNs is 86.5%.

Applied to large datasets, it identified nearly 50,000 AGNs with high reliability.

Abstract

Context. Active galactic nuclei (AGNs) and star forming galaxies (SFGs) are the primary sources of 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) classified the radio sources in LoTSS Deep Fields DR1 through multiwavelength SED fitting. With the classification results of them, we perform 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) are 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, are applied to train the classifier. The performance of the classifier is evaluated mainly by the precission, recall and F1 score of both AGNs and non-AGNs. Results. By comparing the performance of six learning algorithms, CatBoost is chosen to construct the best classifier. The best classifier get precision = 0.974, recall = 0.865 and F1 = 0.916 for AGNs, 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 obtain a sample of 49716 AGNs and 102261 non-AGNs. The reliability of these classification results is confirmed by comparing with the spectroscopic classification of SDSS. The precission and recall of AGN sample can be as high as 94.2% and 92.3%, respectively. We also train a model to identify radio excess sources. The F1 scores are 0.610 and 0.965 for sources with and without radio excess, respectively.