| Automated estimation of stellar fundamental parameters from low resolution spectra： the PLS method |
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| 基金项目： | Supported by the National Natural Science Foundation of China. |
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| 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 （Teff, 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 Teff, 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 1 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 ～ G stars： for flux calibrated spectra, the systematic offsets are 3.8%, 0.14 dex, and -0.09 dex for Teff, 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%, 0.12dex, and -0.13 dex for Teff, 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.
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| 关 键 词： | 偏最小二乘法 恒星光谱参数 回归函数 散射 |
Automated estimation of stellar fundamental parameters from low resolution spectra： the PLS method |
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| Authors: | Jian-Nan Zhang A-Li Luo Yong-Heng Zhao |
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| Institution: | National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China |
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| Abstract: | methods data analysis -- methods statistical -- stars fundamental param- eters （classification, temperatures, metallicity） -- techniques spectroscopic -- surveys |
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| Keywords: | methods data analysis -- methods statistical -- stars fundamental param- eters （classification temperatures metallicity） -- techniques spectroscopic -- surveys |
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