A PCA approach to stellar abundances I. testing of the method validity

The derivation of element abundances of stars is a key step in detailed spectroscopic analysis. A spectroscopic method may suffer from errors associated with model simplifications. We have developed a new method of deriving the various element abundances of stars based on the calibration established from a group of standard stars. We perform principal component analysis (PCA) on a homogeneous library of stellar spectra, and then use machine learning to calibrate the relationship between principal components and element abundances. By testing with spectral libraries S4N and MILES, we find that our procedure provides good consistency when spectra from a homogeneous set of observations are used, and it could be expanded to stars with quite a wide range of stellar parameters, with both dwarfs and giants. Moreover, we discuss the four key factors that have a significant impact on the results of derived element abundances, including the resolution of the spectra, wavelength range, the signal-to-noise ratio (S/N) of spectra and the number of principal components adopted.