中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 国家天文台
  3. 中国科学院国家天文台
A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best-Heckman Sample

文献类型：期刊论文

作者Ma, Zhixian1; Xu, Haiguang2,3,4; Zhu, Jie1; Hu, Dan4; Li, Weitian4; Shan, Chenxi4; Zhu, Zhenghao4; Gu, Liyi5; Li, Jinjin6; Liu, Chengze4
刊名ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
出版日期2019-02-01
卷号240期号:2页码:21
关键词catalogs galaxies: statistics methods: data analysis radio continuum: galaxies techniques: miscellaneous
ISSN号0067-0049
DOI10.3847/1538-4365/aaf9a2
英文摘要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 similar to 93% and an averaged sensitivity of similar to 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 similar to 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.
WOS关键词ACTIVE GALACTIC NUCLEI ; GALAXIES CONFIG SAMPLE ; NEURAL-NETWORKS ; SKY ; POPULATIONS ; SIMULATION ; EVOLUTION ; CLUSTERS ; IMAGES
资助项目National Science Foundation of China[11433002] ; National Science Foundation of China[61371147] ; National Science Foundation of China[11835009] ; National Science Foundation of China[11621303] ; National Science Foundation of China[51672176] ; National Science Foundation of China[11673017] ; National Key Research and Discovery Plan[2018YFA0404601] ; National Key Research and Discovery Plan[2017YFF0210903] ; National Key Basic Research Program of China[2015CB857002] ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Shanghai Key Laboratory for Particle Physics and Cosmology
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:000458524100001
出版者IOP PUBLISHING LTD
资助机构National Science Foundation of China ; National Science Foundation of China ; National Key Research and Discovery Plan ; National Key Research and Discovery Plan ; National Key Basic Research Program of China ; National Key Basic Research Program of China ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Shanghai Key Laboratory for Particle Physics and Cosmology ; Shanghai Key Laboratory for Particle Physics and Cosmology ; National Science Foundation of China ; National Science Foundation of China ; National Key Research and Discovery Plan ; National Key Research and Discovery Plan ; National Key Basic Research Program of China ; National Key Basic Research Program of China ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Shanghai Key Laboratory for Particle Physics and Cosmology ; Shanghai Key Laboratory for Particle Physics and Cosmology ; National Science Foundation of China ; National Science Foundation of China ; National Key Research and Discovery Plan ; National Key Research and Discovery Plan ; National Key Basic Research Program of China ; National Key Basic Research Program of China ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Shanghai Key Laboratory for Particle Physics and Cosmology ; Shanghai Key Laboratory for Particle Physics and Cosmology ; National Science Foundation of China ; National Science Foundation of China ; National Key Research and Discovery Plan ; National Key Research and Discovery Plan ; National Key Basic Research Program of China ; National Key Basic Research Program of China ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education ; Shanghai Key Laboratory for Particle Physics and Cosmology ; Shanghai Key Laboratory for Particle Physics and Cosmology
源URL[http://ir.bao.ac.cn/handle/114a11/24954]  
专题中国科学院国家天文台
通讯作者Ma, Zhixian
作者单位1.Shanghai Jiao Tong Univ, Dept Elect Engn, 800 Dongchuan Rd, Shanghai 200240, Peoples R China
2.Shanghai Jiao Tong Univ, Sch Phys & Astron, Tsung Dao Lee Inst, 800 Dongchuan Rd, Shanghai 200240, Peoples R China
3.Shanghai Jiao Tong Univ, IFSA Collaborat Innovat Ctr, Shanghai 200240, Peoples R China
4.Shanghai Jiao Tong Univ, Dept Astron, 800 Dongchuan Rd, Shanghai 200240, Peoples R China
5.SRON, Netherlands Inst Space Res, Sorbonnelaan 2, NL-3584 CA Utrecht, Netherlands
6.Shanghai Jiao Tong Univ, Dept Micro Nano Elect, Key Lab Thin Film & Microfabricat Technol, Minist Educ, Shanghai 200240, Peoples R China
7.Chinese Acad Sci, Natl Astron Observ, 20A Datun Rd, Beijing 100012, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Zhixian,Xu, Haiguang,Zhu, Jie,et al. A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best-Heckman Sample[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2019,240(2):21.
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.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,240(2),21.
MLA Ma, Zhixian,et al."A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best-Heckman Sample".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 240.2(2019):21.

入库方式： OAI收割

来源：国家天文台

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。