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20122019
most citedA Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected From The Best-Heckman Sample

58 citations · 183 across the 12 of their papers we have counts for

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Showing 2018 · cs.CVShow all

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cs.CV2018★ 1 cited

Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models

Zhixian Ma, Jie Zhu, Weitian Li +1

The morphology of a radio galaxy is highly affected by its central active galactic nuclei (AGN), which is studied to reveal the evolution of the super massive black hole (SMBH). In…