{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/radio-galaxy-morphology-generation-using-dnn","title":"Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models","arxiv_id":"1806.00398","date":"2018-06-01","proceeding":null,"authors":["Zhixian Ma","Jie Zhu","Weitian Li","Haiguang Xu"],"abstract":"The morphology of a radio galaxy is highly affected by its central active\ngalactic nuclei (AGN), which is studied to reveal the evolution of the super\nmassive black hole (SMBH). In this work, we propose a morphology generation\nframework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and\ntype-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian\nmixture models (GMMs). The encoder and decoder subnets in the DNNAE are\nsymmetric aside a fully-connected layer namely code layer hosting the extracted\nfeature vectors. By randomly generating the feature vectors later with a\nthree-component Gaussian Mixture models, new FRI or FRII radio galaxy\nmorphologies are simulated. Experiments were demonstrated on real radio galaxy\nimages, where we discussed the length of feature vectors, selection of lost\nfunctions, and made comparisons on batch normalization and dropout techniques\nfor training the network. The results suggest a high efficiency and performance\nof our morphology generation framework. Code is available at:\nhttps://github.com/myinxd/dnnae-gmm.","url_abs":"http://arxiv.org/abs/1806.00398v1","url_pdf":"http://arxiv.org/pdf/1806.00398v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"radio-galaxy-morphology-generation-using-dnn","repo_url":"https://github.com/myinxd/dnnae-gmm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}