A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best–Heckman Sample

We present a morphological classification of 14,245 radio active galactic nuclei (AGNs) into six types, i. e., typical Fanaroff-Riley Class I/II (FRI/II), FRI/II-like bent-tailed, X-shaped radio galaxy, and ringlike radio galaxy, by designing a convolutional neural network based autoencoder, namely MCRGNet, and applying it to a labeled radio galaxy (LRG) sample containing 1442 AGNs and an unlabeled radio galaxy (unLRG) sample containing 14,245 unlabeled AGNs selected from the Best-Heckman sample. We train MCRGNet and implement the classification task by a three-step strategy, i. e., pre-training, fine-tuning, and classification, which combines both unsupervised and supervised learnings. A four-layer dichotomous tree is designed to classify the radio AGNs, which leads to a significantly better performance than the direct six-type classification. On the LRG sample, our MCRGNet achieves a total precision of similar to 93% and an averaged sensitivity of similar to 87%, which are better than those obtained in previous works. On the unLRG sample, whose labels have been human-inspected, the neural network achieves a total precision of similar to 80%. Also, using Sloan Digital Sky Survey Data Release 7 to calculate the r-band absolute magnitude (Mopt) and using the flux densities to calculate the radio luminosity (Lradio), we find that the distributions of the unLRG sources on the Lradio-Mopt plane do not show an apparent redshift evolution and could confirm with a sufficiently large sample that there could not exist an abrupt separation between FRIs and FRIIs as reported in some previous works.