http://arxiv.org/abs/1812.07190
We present a morphological classification of 14,251 radio active galactic nuclei (AGNs) into six types, i.e., typical Fanaroff-Riley Class I / II (FRI/II), FRI/II-like bent tailed (BT), X-shaped radio galaxy (XRG), and ring-like radio galaxy (RRG), by designing a convolutional neural network (CNN) based autoencoder (CAE), namely MCRGNet, and applying it to a labeled radio galaxy (LRG) sample containing 1,442 AGNs and an unlabled radio galaxy (unLRG) sample containing 14,251 unlabeled AGNs selected from the Best-Heckman sample. We train the 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 \$\sim 93\%\$ and an averaged sensitivity of \$\sim 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 \$\sim 84\%\$. Also, by using the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) to calculate the $r$-band absolute magnitude (\$M_\mathrm{opt}\$), and using the flux densities to calculate the radio luminosity (\$L_\mathrm{radio}\$), we find that the distributions of the unLRG sources on the \$L_\mathrm{radio}\$-\$M_\mathrm{opt}\$ 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.
Z. Ma, H. Xu, J. Zhu, et. al.
Wed, 19 Dec 18
19/84
Comments: Accepted by ApJS. The full table of the catalog and code for our network can be downloaded from this https URL
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