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. 云南天文台
  3. 中国科学院云南天文台
  4. 丽江天文观测站（南方基地）
Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5

文献类型：期刊论文

作者He, Zizhao19; Li, Rui18; Shu, Yiping19; Tortora, Crescenzo17; Er, Xinzhong16; Cañameras, Raoul13,14,15; Schuldt, Stefan11,12; Napolitano, Nicola R.8,9,10; N, Bharath Chowdhary7; Chen, Qihang5,6
刊名ASTROPHYSICAL JOURNAL
出版日期2025-03-10
卷号981期号:2
ISSN号0004-637X
DOI10.3847/1538-4357/adaf28
产权排序第17完成单位
文献子类Article
英文摘要Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous data sets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a convolutional neural network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5. Our approach involves three key stages: first, we preselected 10 million bright objects (with r-band MAG_AUTO < 22), excluding stars from the data set; second, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false-positive rate of similar to 1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; third, human inspections were performed for further selections, and then, 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online (https://github.com/EigenHermit/H24). We have also included an excellent quad candidate in the Appendix, discovered serendipitously during the fine-tuning process of the CNN.
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KILO-DEGREE SURVEY ; STRONG GRAVITATIONAL LENSES ; HSC IMAGING SUGOHI ; DIGITAL SKY SURVEY ; BROAD-LINE REGION ; SPACE-TELESCOPE ; HUBBLE CONSTANT ; INDEPENDENT DETERMINATION ; CIRCUMGALACTIC MEDIUM ; SPECTROSCOPY SURVEY
资助项目China Postdoctoral Foundation Project divided by National Postdoctoral Program for Innovative Talents (Postdoctoral Innovation Talent Support Program of China)https://doi.org/10.13039/501100012152
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001439096100001
出版者IOP Publishing Ltd
资助机构China Postdoctoral Foundation Project divided by National Postdoctoral Program for Innovative Talents (Postdoctoral Innovation Talent Support Program of China)https://doi.org/10.13039/501100012152
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/28193]  
专题云南天文台_丽江天文观测站（南方基地）
作者单位1.Ruhr University Bochum, Faculty of Physics and Astronomy, Astronomical Institute (AIRUB), German Centre for Cosmological Lensing, 44780 Bochum, Germany
2.School of Astronomy and Space Sciences, University of Science and Technology of China, Hefei 230026, People's Republic of China;
3.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, Yunnan, People's Republic of China;
4.Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, CAS, Beijing 100101, People's Republic of China;
5.Institute for Frontier in Astronomy and Astrophysics, Beijing Normal University, Beijing, 102206, People's Republic of China;
6.Department of Astronomy, Beijing Normal University, Beijing 100875, People's Republic of China;
7.Kapteyn Astronomical Institute, University of Groningen, PO Box 800, NL-9700 AV Groningen, The Netherlands;
8.INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, 80131 - Napoli, Italy;
9.CSST Science Center for Guangdong-Hong Kong-Macau Great Bay Area, Zhuhai, 519082, People's Republic of China;
10.School of Physics and Astronomy, Sun Yat-sen University, Zhuhai Campus, 2 Daxue Road, Xiangzhou District, Zhuhai, People's Republic of China;
推荐引用方式
GB/T 7714		 He, Zizhao,Li, Rui,Shu, Yiping,et al. Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5[J]. ASTROPHYSICAL JOURNAL,2025,981(2).
APA He, Zizhao.,Li, Rui.,Shu, Yiping.,Tortora, Crescenzo.,Er, Xinzhong.,...&Dvornik, Andrej.(2025).Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5.ASTROPHYSICAL JOURNAL,981(2).
MLA He, Zizhao,et al."Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5".ASTROPHYSICAL JOURNAL 981.2(2025).

入库方式： OAI收割

来源：云南天文台

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。