Stellar parameter extraction based on template matching and Monte-carlo simulation

Astronomical spectra contain a wealth of physical information of the distant celestial bodies. The massive spectral datasets produced by large sky survey provide us with more chances to carry out many relevant research. One of them is to extract the fundamental parameters of stellar atmosphere: the effective temperature (Teff), surface gravity (Logg), and metal abundance ([Fe/H]) from astronomical spectra. To do this, a matching function between observed spectrum and theoretical spectrum is defined. The NELDER-MEAD algorithm is used to search for the minimal value of the function in the three-dimensional space constructed by Teff, logg and [Fe/H]. Parameters derived from the best-matched template are used as parameters of the measured spectrum. Noise distribution is simulated by the Monte-carlo method in order to calculate parameter errors. Results of the LAMOST pilot survey data and the released results of SDSS are compared and analyzed. Experimental results show that this method is effective and can be applied to LAMOST of China. Copyright © 2015 Binary Information Press.