Automatic stellar spectra parameterisation in the IR CaII triplet region

Details Automatic stellar spectra parameterisation in the IR CaII triplet region Automatic stellar spectra parameterisation in the IR CaII triplet region

2011-09-28

arxiv.org/abs/1109.6237v1

Astronomy and Astrophysics

Astrophysics

Instrumentation and Methods for Astrophysics

17 pages, 11 figures, accepted for publication in A&A

Scientific paper

(Abridged) Galactic archaeology aims to determine the evolution of the Galaxy from the chemical and kinematical properties of its stars. The analysis of current large spectroscopic surveys (thousands of stars) and future ones (millions of stars) require automated analysis techniques to obtain robust estimates of the stellar parameters. Several on-going and planned spectroscopic surveys have selected their wavelength region to contain the IR CaII triplet and this paper focuses on the automatic analysis of such spectra. We investigated two algorithms, MATISSE and DEGAS, both of which compare the observed spectrum to a grid of synthetic spectra, but each uses a different mathematical approach for finding the optimum match and hence the best stellar parameters. We identified degeneracies in different regions of the HR diagram: hot dwarfs and giants share the same spectral signatures. Furthermore, the surface gravity of cooler dwarfs is difficult to determine accurately. These effects are intensified when the information decreases (e.g. metal-poor stars or low SNR spectra). Our results show that the local projection method MATISSE is preferred for high SNR spectra, whereas the decision-tree method DEGAS is preferred for noisier spectra. We therefore propose a hybrid approach of both methods and show that sufficiently accurate results for the purposes of galactic archaeology are retrieved down to SNR~20 for typical thin or thick disc stars, and down to SNR~50 for the more metal-poor halo giants. If unappreciated, degeneracies in stellar parameters can introduce biases in derived quantities for target stars such as distances and full space motions. These biases can be minimised using the knowledge gained by thorough testing of the proposed algorithm, which in turn lead to robust automated methods for the coming extensive stellar spectroscopic surveys in the Local Group.

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