Validation of LAMOST stellar parameters with the PASTEL catalog

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） published its first data release （DR1） in 2013, which is currently the largest dataset of stellar spectra in the world. We combine the PASTEL catalog and SIMBAD radial velocities as a testing standard to validate stellar parameters （effec- tive temperature Tefr, surface gravity log g, metallicity [Fe/H] and radial velocity Vr） derived from DR1. Through cross-identification of the DR1 catalogs and the PASTEL catalog, we obtain a preliminary sample of 422 stars. After removal of stellar param- eter measurements from problematic spectra and applying effective temperature con- straints to the sample, we compare the stellar parameters from DR1 with those from PASTEL and SIMBAD to demonstrate that the DR1 results are reliable in restricted ranges of Tefr. We derive standard deviations of 110 K, 0.19 dex and 0.11 dex for Tell, log 9 and [Fe/H] respectively when Teff〈 8000 K, and 4.91 km s-1 for Vr when Teff 〈 10 000 K. Systematic errors are negligible except for those of Vr. In addition, metallicities in DR1 are systematically higher than those in PASTEL, in the range of PASTEL [Fe/H] 〈 -1.5.

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.

Stellar spectra association rule mining method based on the weighted frequent pattern tree

Effective extraction of data association rules can provide a reliable basis for classification of stellar spectra. The concept of stellar spectrum weighted itemsets and stellar spectrum weighted association rules are introduced, and the weight of a single property in the stellar spectrum is determined by information entropy. On that basis, a method is presented to mine the association rules of a stellar spectrum based on the weighted frequent pattern tree. Important properties of the spectral line are highlighted using this method. At the same time, the waveform of the whole spectrum is taken into account. The experimental results show that the data association rules of a stellar spectrum mined with this method are consistent with the main features of stellar spectral types.