Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models

1 Jun 2018Zhixian MaJie ZhuWeitian LiHaiguang Xu

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 this work, we propose a morphology generation framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian mixture models (GMMs)... (read more)

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