Estimating stellar atmospheric parameters based on Lasso features |
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| Authors: | Chuan-Xing Liu Pei-Ai Zhang Yu Lu |
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| Institution: | [1]Department of Mathematics, Jinan University, Guangzhou 510632, China; [2]Scfiool of Mathematical Sciences, South China Normal Universityl Guangziaou 510631, China |
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| Abstract: | With the rapid development of large scale sky surveys like the Sloan Digital Sky Survey(SDSS), GAIA and LAMOST(Guoshoujing telescope), stellar spectra can be obtained on an ever-increasing scale. Therefore, it is necessary to estimate stellar atmospheric parameters such as Teff, log g and Fe/H] automatically to achieve the scientific goals and make full use of the potential value of these observations. Feature selection plays a key role in the automatic measurement of atmospheric parameters.We propose to use the least absolute shrinkage selection operator(Lasso) algorithm to select features from stellar spectra. Feature selection can reduce redundancy in spectra,alleviate the influence of noise, improve calculation speed and enhance the robustness of the estimation system. Based on the extracted features, stellar atmospheric parameters are estimated by the support vector regression model. Three typical schemes are evaluated on spectral data from both the ELODIE library and SDSS. Experimental results show the potential performance to a certain degree. In addition, results show that our method is stable when applied to different spectra. |
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| Keywords: | methods： data analysis -- stars： fundamental parameters -- techniques：spectroscopic -- surveys |
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