NAOC Open IR
Telescope performance real-time monitoring based on machine learning
Hu, Tian Z.1,2,3; Zhang, Yong1,2; Cui, Xiang Q.1,2; Zhang, Qing Y.1,2,4; Li, Ye P.1,2; Cao, Zi H.3,5; Pan, Xiu S.1,2,3; Fu, Ying1,2,3
2021
Source PublicationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
ISSN0035-8711
Volume500Issue:1Pages:388-396
AbstractIn astronomy, the demand for high-resolution imaging and high-efficiency observation requires telescopes that are maintained at peak performance. To improve telescope performance, it is useful to conduct real-time monitoring of the telescope status and detailed recordings of the operational data of the telescope. In this paper, we provide amethod based on machine learning to monitor the telescope performance in real-time. First, we use picture features and the random forest algorithm to select normal pictures captured by the acquisition camera or science camera. Next, we cut out the source image of the picture and use convolutional neural networks to recognize star shapes. Finally, we monitor the telescope performance based on the relationship between the source image shape and telescope performance. Through this method, we achieve high-performance real-timemonitoring with the Large Sky Area Multi-Object Fibre Spectroscopic Telescope, including guiding system performance, focal surface defocus, submirror performance, and active optics system performance. The ultimate performance detection accuracy can reach up to 96.7 per cent.
Keywordmethods: analytical methods: data analysis methods: statistical techniques: image processing
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission
DOI10.1093/mnras/staa3087
WOS KeywordDIGITAL SKY SURVEY ; METALLICITY ; NETWORKS
Language英语
Funding ProjectNational Natural Science Foundation of China[U1931207] ; National Natural Science Foundation of China[U2031207] ; National Natural Science Foundation of China[U1931126] ; National Natural Science Foundation of China[12073047] ; National Development and Reform Commission
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000599134600030
PublisherOXFORD UNIV PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/80259
Collection中国科学院国家天文台
Corresponding AuthorZhang, Yong; Cui, Xiang Q.
Affiliation1.Chinese Acad Sci, Natl Astron Observ, Nanjing Inst Astron Opt & Technol, Nanjing 210042, Peoples R China
2.Nanjing Inst Astron Opt & Technol, CAS Key Lab Astron Opt & Technol, Nanjing 210042, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Univ Sci & Technol China, Hefei 230026, Peoples R China
5.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Hu, Tian Z.,Zhang, Yong,Cui, Xiang Q.,et al. Telescope performance real-time monitoring based on machine learning[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2021,500(1):388-396.
APA Hu, Tian Z..,Zhang, Yong.,Cui, Xiang Q..,Zhang, Qing Y..,Li, Ye P..,...&Fu, Ying.(2021).Telescope performance real-time monitoring based on machine learning.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,500(1),388-396.
MLA Hu, Tian Z.,et al."Telescope performance real-time monitoring based on machine learning".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 500.1(2021):388-396.
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