NAOC Open IR
A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best–Heckman Sample
Ma,Zhixian1; Xu,Haiguang2,3,4; Zhu,Jie1; Hu,Dan4; Li,Weitian4; Shan,Chenxi4; Zhu,Zhenghao4; Gu,Liyi5; Li,Jinjin6; Liu,Chengze4; Wu,Xiangping7
2019-02-12
Source PublicationThe Astrophysical Journal Supplement Series
ISSN0067-0049
Volume240Issue:2
AbstractAbstract 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, X-shaped radio galaxy, and ringlike radio galaxy, by designing a convolutional neural network based autoencoder, namely MCRGNet, and applying it to a labeled radio galaxy (LRG) sample containing 1442 AGNs and an unlabeled radio galaxy (unLRG) sample containing 14,245 unlabeled AGNs selected from the Best–Heckman sample. We train 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 ~93% and an averaged sensitivity of ~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 ~80%. Also, using Sloan Digital Sky Survey Data Release 7 to calculate the r-band absolute magnitude (Mopt) and using the flux densities to calculate the radio luminosity (Lradio), we find that the distributions of the unLRG sources on the Lradio–Mopt 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.
Keywordcatalogs galaxies: statistics methods: data analysis radio continuum: galaxies techniques: miscellaneous
DOI10.3847/1538-4365/aaf9a2
Language英语
WOS IDIOP:0067-0049-240-2-aaf9a2
PublisherThe American Astronomical Society
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/35482
Collection中国科学院国家天文台
Affiliation1.Department of Electronic Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang, Shanghai 200240, People’s Republic of China mazhixian@sjtu.edu.cn, zhujie@sjtu.edu.cn
2.School of Physics and Astronomy/Tsung-Dao Lee Institute, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang, Shanghai 200240, People’s Republic of China; hgxu@sjtu.edu.cn
3.IFSA Collaborative Innovation Center, Shanghai Jiao Tong University, Minhang, Shanghai 200240, People’s Republic of China
4.Department of Astronomy, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang, Shanghai 200240, People’s Republic of China
5.SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands
6.Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
7.National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100012, People’s Republic of China
Recommended Citation
GB/T 7714
Ma,Zhixian,Xu,Haiguang,Zhu,Jie,等. A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best–Heckman Sample[J]. The Astrophysical Journal Supplement Series,2019,240(2).
APA Ma,Zhixian.,Xu,Haiguang.,Zhu,Jie.,Hu,Dan.,Li,Weitian.,...&Wu,Xiangping.(2019).A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best–Heckman Sample.The Astrophysical Journal Supplement Series,240(2).
MLA Ma,Zhixian,et al."A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best–Heckman Sample".The Astrophysical Journal Supplement Series 240.2(2019).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ma,Zhixian]'s Articles
[Xu,Haiguang]'s Articles
[Zhu,Jie]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ma,Zhixian]'s Articles
[Xu,Haiguang]'s Articles
[Zhu,Jie]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ma,Zhixian]'s Articles
[Xu,Haiguang]'s Articles
[Zhu,Jie]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.