938,720 Giants from LAMOST I: Determination of Stellar Parameters and α, C, N Abundances with Deep Learning | |
Zhang,X.1,2; Zhao,G.1,2; Yang,C. Q.1,2; Wang,Q. X.1,2; Zuo,W. B.1,2 | |
刊名 | Publications of the Astronomical Society of the Pacific
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2019-07-19 | |
卷号 | 131期号:1003 |
关键词 | Galaxy: evolution stars: abundances techniques: spectroscopic |
ISSN号 | 0004-6280 |
DOI | 10.1088/1538-3873/ab2687 |
英文摘要 | Abstract Extracting accurate atmospheric parameters and elemental abundances from stellar spectra is crucial for studying the Galactic evolution. In this paper, a deep neural network architecture named StarNet is used to estimate stellar parameters (Teff, log g, [M/H]), α-elements as well as C and N abundances from LAMOST spectra, using stars in common with APOGEE survey as training data set. With the spectral signal-to-noise ratio (S/N) in g band (S/Ng) larger than 10, the test indicates our method yields uncertainties of 45 K for Teff, 0.1 dex for log g, 0.05 dex for [M/H], 0.03 dex for [α/M], 0.06 dex for [C/M] and 0.07 dex for [N/M]. Because of few stars with [M/H] 1.0 dex in the training set, the uncertainties are dominated by stars with [M/H] > ?1.0 dex. Based on test results, we think StarNet is valid for measuring parameters from low-resolution spectra of the LAMOST survey. The trained network is then used to predict parameters for 938,720 giants from LAMOST DR5. Within the range of stellar parameters 4000 K < Teff?5300 K, 0 dex < log g < 3.8 dex and ?2.5 dex < [M/H] < 0.5 dex, the comparisons with high-precision measurements (e.g., PASTEL, asteroseismic log g) yield uncertainties of 100 K for Teff, 0.10 dex for log g, 0.12 dex for [M/H]. Our estimations are consistent with values from the high-precision measurements. In this research, a deep neural network is successfully applied on the numerous spectra from LAMOST. The deep neural network shows an excellent performance, which demonstrates that deep learning can effectively reduce the inconsistencies between parameters measured by the individual survey pipelines. |
语种 | 英语 |
出版者 | The Astronomical Society of the Pacific |
WOS记录号 | IOP:0004-6280-131-1003-AB2687 |
内容类型 | 期刊论文 |
源URL | [http://ir.bao.ac.cn/handle/114a11/28757] ![]() |
专题 | 中国科学院国家天文台 |
作者单位 | 1.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China 2.Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, People's Republic of China gzhao@nao.cas.cn |
推荐引用方式 GB/T 7714 | Zhang,X.,Zhao,G.,Yang,C. Q.,et al. 938,720 Giants from LAMOST I: Determination of Stellar Parameters and α, C, N Abundances with Deep Learning[J]. Publications of the Astronomical Society of the Pacific,2019,131(1003). |
APA | Zhang,X.,Zhao,G.,Yang,C. Q.,Wang,Q. X.,&Zuo,W. B..(2019).938,720 Giants from LAMOST I: Determination of Stellar Parameters and α, C, N Abundances with Deep Learning.Publications of the Astronomical Society of the Pacific,131(1003). |
MLA | Zhang,X.,et al."938,720 Giants from LAMOST I: Determination of Stellar Parameters and α, C, N Abundances with Deep Learning".Publications of the Astronomical Society of the Pacific 131.1003(2019). |
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