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Identifying Compton-thick Active Galactic Nuclei with a Machine Learning Algorithm in Chandra Deep Field-South

文献类型：期刊论文

作者Zhang, Rui7,8,9; Guo, Xiaotong7,8,9; Gu, Qiusheng6,7; Fang, Guanwen8,9; Xu, Jun8,9; Feng HC(封海成)5; Chen, Yongyun4; Li, Rui3; Ding, Nan2; Wang, Hongtao1
刊名ASTROPHYSICAL JOURNAL
出版日期2025-07-01
卷号987期号:1
ISSN号0004-637X
DOI10.3847/1538-4357/addaab
产权排序第5完成单位
文献子类Article
英文摘要Compton-thick active galactic nuclei (CT-AGNs), which are defined by column density NH >= 1.5 x 1024 cm-2, emit feeble X-ray radiation, even undetectable by X-ray instruments. Despite this, the X-ray emissions from CT-AGNs are believed to be a substantial contributor to the cosmic X-ray background (CXB). According to synthesis models of AGNs, CT-AGNs are expected to make up a significant fraction of the AGN population, likely around 30% or more. However, only similar to 11% of AGNs have been identified as CT-AGNs in the Chandra Deep Field-South (CDFS). To identify hitherto unknown CT-AGNs in the field, we used a random forest algorithm for identifying them. First, we build a secure classified subset of 210 AGNs to train and evaluate our algorithm. Our algorithm achieved an accuracy rate of 90% on the test set after training. Then, we applied our algorithm to an additional subset of 254 AGNs, successfully identifying 67 CT-AGNs within this group. This result significantly increased the fraction of CT-AGNs in the CDFS, which is closer to the theoretical predictions of the CXB. Finally, we compared the properties of host galaxies between CT-AGNs and non-CT-AGNs and found that the host galaxies of CT-AGNs exhibit higher levels of star formation activity.
学科主题天文学 ; 星系与宇宙学
URL标识查看原文
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
WOS关键词SUPERMASSIVE BLACK-HOLES ; X-RAY ; OBSCURED AGN ; XMM-NEWTON ; JOINT NUSTAR ; FRACTION ; MODEL ; POPULATION ; ABSORPTION ; LUMINOSITY
资助项目MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[12203050]; National Nature Science Foundation of China[2308085QA33]; Anhui Provincial Natural Science Foundation; Open Fund Project of Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University)[12203096]; National Natural Science Foundation of China[2024AH051097]; Natural Science Research Project of Anhui Educational Committee[2081450001]; Yunnan Province[A2022408002]; Hebei Natural Science Foundation of China
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001517698100001
出版者IOP Publishing Ltd
资助机构MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[12203050] ; National Nature Science Foundation of China[2308085QA33] ; Anhui Provincial Natural Science Foundation ; Open Fund Project of Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University)[12203096] ; National Natural Science Foundation of China[2024AH051097] ; Natural Science Research Project of Anhui Educational Committee[2081450001] ; Yunnan Province[A2022408002] ; Hebei Natural Science Foundation of China
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/28402]  
专题云南天文台_丽江天文观测站（南方基地）
通讯作者Guo, Xiaotong
作者单位1.School of Science, Langfang Normal University, Langfang 065000, People’s Republic of China
2.School of Physical Science and Technology, Kunming University, Kunming 650214, People’s Republic of China;
3.Institude for Astrophysics, School of Physics, Zhengzhou University, Zhengzhou, 450001, People’s Republic of China;
4.College of Physics and Electronic Engineering, Qujing Normal University, Qujing 655011, People’s Republic of China;
5.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, People’s Republic of China;
6.School of Astronomy and Space Science, Nanjing University, Nanjing, Jiangsu 210093, People’s Republic of China; qsgu@nju.edu.cn;
7.Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education, Nanjing 210093, People’s Republic of China;
8.Institute of Astronomy and Astrophysics, Anqing Normal University, Anqing 246133, People’s Republic of China;
9.School of Mathematics and Physics, Anqing Normal University, Anqing 246133, People’s Republic of China; guoxiaotong@aqnu.edu.cn;
推荐引用方式
GB/T 7714		 Zhang, Rui,Guo, Xiaotong,Gu, Qiusheng,et al. Identifying Compton-thick Active Galactic Nuclei with a Machine Learning Algorithm in Chandra Deep Field-South[J]. ASTROPHYSICAL JOURNAL,2025,987(1).
APA Zhang, Rui.,Guo, Xiaotong.,Gu, Qiusheng.,Fang, Guanwen.,Xu, Jun.,...&Wang, Hongtao.(2025).Identifying Compton-thick Active Galactic Nuclei with a Machine Learning Algorithm in Chandra Deep Field-South.ASTROPHYSICAL JOURNAL,987(1).
MLA Zhang, Rui,et al."Identifying Compton-thick Active Galactic Nuclei with a Machine Learning Algorithm in Chandra Deep Field-South".ASTROPHYSICAL JOURNAL 987.1(2025).

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