most citedA Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected From The Best-Heckman Sample

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astro-ph.GA2018★ 58 cited

A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected From The Best-Heckman Sample

Zhixian Ma, Haiguang Xu, Jie Zhu +8

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…