A spectral analysis pipeline of LAMOST (Large sky Area Multi-Object fiber Spectroscopic Telescope), which produces archived spectral type data, is introduced. By studying observational and theoretical stellar spectra, spectral features within medium resolution are discussed, those lines and bands with high sensitivity to stellar atmospheric parameters, viz. effective temperature (Teff), surface gravity (logg) and metallicity ([Fe/H]), were selected. According to the research, selected features were put into different objective algorithms to extract parameters. The application of three algorithms to SDSS/SEGUE spectra, namely radial basis function neural network (RBFN), back propagation neural network (BPN) and non-parameter regression (NPR), shows intrinsic statistical consistency. Based on the above research, a stellar atmospheric parameter pipeline for LAMOST is designed.

Date Published: 16 July 2008
PDF: 11 pages
Proc. SPIE 7019, Advanced Software and Control for Astronomy II, 701935 (16 July 2008); doi: 10.1117/12.788251

Published in SPIE Proceedings Vol. 7019:
Advanced Software and Control for Astronomy II
Alan Bridger; Nicole M. Radziwill, Editor(s)