Automated estimation of stellar fundamental parameters from low resolution spectra: the PLS method
文献类型：期刊论文
| 作者 | Zhang, Jian-Nan ; Luo, A-Li; Zhao, Yong-Heng
|
| 刊名 | RESEARCH IN ASTRONOMY AND ASTROPHYSICS
 |
| 出版日期 | 2009-06-01 |
| 卷号 | 9期号:6页码:712-724 |
| 关键词 | methods: data analysis methods: statistical stars: fundamental parameters (classification, temperatures, metallicity) techniques: spectroscopic surveys |
| 英文摘要 | PLS (Partial Least Squares regression) is introduced into an automatic estimation of fundamental stellar spectral parameters. It extracts the most correlative spectral component to the parameters (T-eff, log g and [Fe/H]), and sets up a linear regression function from spectra to the corresponding parameters. Considering the properties of stellar spectra and the PLS algorithm, we present a piecewise PLS regression method for estimation of stellar parameters, which is composed of one PLS model for T-eff, and seven PLS models for log g and [Fe[H] estimation. Its performance is investigated by large experiments on flux calibrated spectra and continuum normalized spectra at different signal-to-noise ratios (SNRs) and resolutions. The results show that the piecewise PLS method is robust for spectra at the medium resolution of 0.23 nm. For low resolution 0.5 nm and I nm spectra, it achieves competitive results at higher SNR. Experiments using ELODIE spectra of 0.23 nm resolution illustrate that our piecewise PLS models trained with MILES spectra are efficient for O similar to G stars: for flux calibrated spectra, the systematic offsets are 3.8%, 0.14 dex, and -0.09 dex for T-eff, log g and [Fe/H], with error scatters of 5.2%, 0.44 dex and 0.38 dex, respectively; for continuum normalized spectra, the systematic offsets are 3.8%, 0.12 dex, and -0.13 dex for T-eff, log g and (Fe/H], with error scatters of 5.2%, 0.49 dex and 0.41 dex, respectively. The PLS method is rapid, easy to use and does not rely as strongly on the tightness of a parameter grid of templates to reach high precision as Artificial Neural Networks or minimum distance methods do. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000267180100010 |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/7521]  |
| 专题 | 国家天文台_光学天文研究部 |
| 作者单位 | Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Jian-Nan,Luo, A-Li,Zhao, Yong-Heng. Automated estimation of stellar fundamental parameters from low resolution spectra: the PLS method[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2009,9(6):712-724. |
| APA | Zhang, Jian-Nan,Luo, A-Li,&Zhao, Yong-Heng.(2009).Automated estimation of stellar fundamental parameters from low resolution spectra: the PLS method.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,9(6),712-724. |
| MLA | Zhang, Jian-Nan,et al."Automated estimation of stellar fundamental parameters from low resolution spectra: the PLS method".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 9.6(2009):712-724. |
入库方式： OAI收割
来源：国家天文台
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