Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks

doi:10.1088/1538-3873/aaf25f

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	Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks
	Wang Rui 1,2,3; Luo A-li 2,3; Zhang Shuo 1,2,3; Hou Wen 2,3; Du Bing 2,3; Song Yihan 2,3; Wu Kefei 2,3; Chen Jianjun 2,3; Zuo Fang 2,3; Qin Li 1,2,3
刊名	PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC
	2019-02-01
卷号	131 期号:996 页码:13
关键词	methods: data analysis techniques: spectroscopic stars: atmospheres
ISSN号	0004-6280
DOI	10.1088/1538-3873/aaf25f
英文摘要	In this study, the fundamental stellar atmospheric parameters (T-eff, log g, [Fe/H], and [alpha/Fe]) were derived for low-resolution spectroscopy from LAMOST DR5 with generative spectrum networks (GSN). This follows the same scheme as a normal artificial neural network with stellar parameters as the input and spectra as the output. The GSN model was effective in producing synthetic spectra after training on the PHOENIX theoretical spectra. In combination with Bayes framework, the application for analysis of LAMOST observed spectra exhibited improved efficiency on the distributed-computing platform, Spark. In addition, the results were examined and validated by a comparison with reference parameters from high-resolution surveys and asteroseismic results. Our results show good consistency with the results from other surveys and catalogs. Our proposed method is reliable with a precision of 80 K for T-eff, 0.14 dex for log g, 0.07 dex for [Fe/H] and 0.168 dex for [alpha/Fe], for spectra with a signal-to-noise (S/N) in g bands (S/N-g) higher than 50. The parameters estimated as a part of this work are available at. http://paperdata.china-vo.org/GSN_parameters/GSN_parameters.csv.
资助项目	National Key Basic Research Program of China[2014CB845700] ; National Natural Science Foundation of China[11390371] ; National Development and Reform Commission
WOS关键词	GENEVA-COPENHAGEN SURVEY ; ATMOSPHERIC PARAMETERS ; MODEL ATMOSPHERES ; SPECTROSCOPIC SURVEY ; SOLAR NEIGHBORHOOD ; STARS ; SEGUE ; GAIA ; PARAMETRIZATION ; PERFORMANCE
WOS研究方向	Astronomy & Astrophysics
语种	英语
出版者	IOP PUBLISHING LTD
WOS记录号	WOS:000455937400001
资助机构	National Key Basic Research Program of China ; National Key Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Key Basic Research Program of China ; National Key Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Key Basic Research Program of China ; National Key Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission ; National Key Basic Research Program of China ; National Key Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Development and Reform Commission ; National Development and Reform Commission
内容类型	期刊论文
源URL	[http://ir.bao.ac.cn/handle/114a11/24703]
专题	中国科学院国家天文台
通讯作者	Luo A-li
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China 3.Chinese Acad Sci, Natl Astron Observ, Key Lab Opt Astron, Beijing 100012, Peoples R China
推荐引用方式 GB/T 7714	Wang Rui,Luo A-li,Zhang Shuo,et al. Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks[J]. PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC,2019,131(996):13.
APA	Wang Rui.,Luo A-li.,Zhang Shuo.,Hou Wen.,Du Bing.,...&Lu Yan.(2019).Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks.PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC,131(996),13.
MLA	Wang Rui,et al."Analysis of Stellar Spectra from LAMOST DR5 with Generative Spectrum Networks".PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC 131.996(2019):13.