Automatic determination of stellar atmospheric parameters and construction of stellar spectral templates of the Guoshoujing Telescope (LAMOST)

A number of spectroscopic surveys have been carried out or are planned to study the origin of the Milky Way. Their exploitation requires reliable automated methods and softwares to measure the fundamental parameters of the stars. Adopting the ULySS package, we have tested the effect of different resolutions and signal-to- noise ratios （SNR） on the measurement of the stellar atmospheric parameters （effective temperature Teff, surface gravity log g, and metaUicity [Fe/H]）. We show that ULySS is reliable for determining these parameters with medium-resolution spectra （R ～2000）. Then, we applied the method to measure the parameters of 771 stars selected in the commissioning database of the Guoshoujing Telescope （LAMOST）. The results were compared with the SDSS/SEGUE Stellar Parameter Pipeline （SSPP）, and we derived precisions of 167 K, 0.34dex, and 0.16dex for Teff, logg and [Fe/H] respectively. Furthermore, 120 of these stars are selected to construct the primary stellar spectral template library （Version 1.0） of LAMOST, and will be deployed as basic ingredients for the LAMOST automated parametrization pipeline.

Estimating stellar atmospheric parameters based on Lasso features

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 stel- lar 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 param- eters 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.

M-giant star candidates identified in LAMOST DR 1

We perform a discrimination procedure with the spectral index diagram of TiO 5 and Ca H2＋Ca H3 to separate M giants from M dwarfs. Using the M giant spectra identified from LAMOST DR1 with high signal-to-noise ratio, we have successfully assembled a set of M giant templates, which show more reliable spectral features. Combining with the M dwarf/subdwarf templates in Zhong et al., we present an extended library of M-type templates which includes not only M dwarfs with a well-defined temperature and metallicity grid but also M giants with subtypes from M0 to M6. Then, the template-fitting algorithm is used to automatically identify and classify M giant stars from LAMOST DR1. The resulting catalog of M giant stars is cross-matched with 2MASS J H Ks and WISE W1/W2 infrared photometry. In addition, we calculated the heliocentric radial velocity of all M giant stars by using the cross-correlation method with the template spectrum in a zero-velocity rest frame.Using the relationship between the absolute infrared magnitude MJ and our classified spectroscopic subtype, we derived the spectroscopic distance of M giants with uncertainties of about 40%. A catalog of 8639 M giants is provided. As an additional result of this analysis, we also present a catalog of 101 690 M dwarfs/subdwarfs which are processed by our classification pipeline.