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Improve the Search of Very Metal-poor Stars Using the Deep Learning Method
Xie, Jianhang1; Bu, Yude2; Liang, Junchao2; Li, Haining3; Wang, Xilu4,5; Pan, Jingchang1
2021-10-01
Source PublicationASTRONOMICAL JOURNAL
ISSN0004-6256
Volume162Issue:4Pages:10
AbstractVery metal-poor (VMP) stars have [Fe/H] < -2.0 dex. They are among the oldest stars in the universe, and their unique metallicity can help explore the enrichment mechanism and evolutionary history of the chemical elements of stars in the early universe. However, most current stellar parameter estimation methods do not perform well in determining the stellar parameters of VMP stars, which limits our ability to discover and exploit the properties of VMP stars. In this study, we propose a new model based on a convolutional neural network to determine the stellar atmospheric parameters of VMP stars. We tested our model on the LAMOST spectra; our model can determine the effective temperature (T-eff), surface gravity (log g), metallicity ([Fe/H]), and carbon abundance ([C/Fe]) of the LAMOST spectra with precisions sigma(T-eff) = 134.82 K, sigma(log g)= 0.33 dex, sigma([Fe/H]) = 0.20 dex, and sigma([C/Fe]) = 0.35 dex. Furthermore, our model can distinguish VMP stars from normal stars with an accuracy of 88.65%. We also compared this model with other widely used methods, and found that this method performs better than other methods. It can be applied to the stellar parameter pipelines of upcoming large surveys such as 4MOST, WEAVES, and MOONS to search for VMP stars and identify carbon-enhanced metal-poor stars.
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; China Manned Space Project ; China Manned Space Project ; Young Scholars Program of Shandong University, Weihai ; Young Scholars Program of Shandong University, Weihai ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the CAS ; Youth Innovation Promotion Association of the CAS ; NSFC ; NSFC ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; China Manned Space Project ; China Manned Space Project ; Young Scholars Program of Shandong University, Weihai ; Young Scholars Program of Shandong University, Weihai ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the CAS ; Youth Innovation Promotion Association of the CAS ; NSFC ; NSFC ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; China Manned Space Project ; China Manned Space Project ; Young Scholars Program of Shandong University, Weihai ; Young Scholars Program of Shandong University, Weihai ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the CAS ; Youth Innovation Promotion Association of the CAS ; NSFC ; NSFC ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; China Manned Space Project ; China Manned Space Project ; Young Scholars Program of Shandong University, Weihai ; Young Scholars Program of Shandong University, Weihai ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the CAS ; Youth Innovation Promotion Association of the CAS ; NSFC ; NSFC ; National Natural Science Foundation of China ; National Natural Science Foundation of China
DOI10.3847/1538-3881/ac1c7c
WOS KeywordSTELLAR ATMOSPHERIC PARAMETERS ; DIGITAL SKY SURVEY ; DATA RELEASE ; SPECTRA ; RECOGNITION ; ABUNDANCES ; FREQUENCY ; APOGEE
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[11873037] ; China Manned Space Project[CMS-CSST-2021-B05] ; China Manned Space Project[CMS-CSST-2021-A08] ; Young Scholars Program of Shandong University, Weihai[2016WHWLJH09] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB34020205] ; Youth Innovation Promotion Association of the CAS[Y202017] ; NSFC[11988101] ; NSFC[11973049] ; National Natural Science Foundation of China[U19311209]
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; China Manned Space Project ; China Manned Space Project ; Young Scholars Program of Shandong University, Weihai ; Young Scholars Program of Shandong University, Weihai ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the CAS ; Youth Innovation Promotion Association of the CAS ; NSFC ; NSFC ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; China Manned Space Project ; China Manned Space Project ; Young Scholars Program of Shandong University, Weihai ; Young Scholars Program of Shandong University, Weihai ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the CAS ; Youth Innovation Promotion Association of the CAS ; NSFC ; NSFC ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; China Manned Space Project ; China Manned Space Project ; Young Scholars Program of Shandong University, Weihai ; Young Scholars Program of Shandong University, Weihai ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the CAS ; Youth Innovation Promotion Association of the CAS ; NSFC ; NSFC ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; China Manned Space Project ; China Manned Space Project ; Young Scholars Program of Shandong University, Weihai ; Young Scholars Program of Shandong University, Weihai ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the CAS ; Youth Innovation Promotion Association of the CAS ; NSFC ; NSFC ; National Natural Science Foundation of China ; National Natural Science Foundation of China
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000697834400001
PublisherIOP PUBLISHING LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/75703
Collection中国科学院国家天文台
Corresponding AuthorBu, Yude
Affiliation1.Shandong Univ, Sch Mech Elect & Informat Engn, Shandong 264209, Peoples R China
2.Shandong Univ, Sch Math & Stat, Shandong 264209, Peoples R China
3.Chinese Acad Sci, Natl Astron Observator, Key Lab Optic Astron, Beijing 100012, Peoples R China
4.Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA
5.Univ Notre Dame, Dept Phys, Dame, IN 46556 USA
Recommended Citation
GB/T 7714
Xie, Jianhang,Bu, Yude,Liang, Junchao,et al. Improve the Search of Very Metal-poor Stars Using the Deep Learning Method[J]. ASTRONOMICAL JOURNAL,2021,162(4):10.
APA Xie, Jianhang,Bu, Yude,Liang, Junchao,Li, Haining,Wang, Xilu,&Pan, Jingchang.(2021).Improve the Search of Very Metal-poor Stars Using the Deep Learning Method.ASTRONOMICAL JOURNAL,162(4),10.
MLA Xie, Jianhang,et al."Improve the Search of Very Metal-poor Stars Using the Deep Learning Method".ASTRONOMICAL JOURNAL 162.4(2021):10.
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