Automated Stellar Spectral Classification and Param- Eterization for the Masses

Stellar spectroscopic classification has been successfully automated by a number of groups. Automated classification and parameterization work best when applied to a homogeneous data set, and thus these techniques primarily have been developed for and applied to large surveys. While most ongoing large spectroscopic surveys target extragalactic objects, many stellar spectra have been and will be obtained. We briefly summarize past work on automated classification and parameterization, with emphasis on the work done in our group. Accurate automated classification in the spectral type domain and parameterization in the temperature domain have been relatively easy. Automated parameterization in the metallicity domain, formally outside the MK system, has also been effective. Due to the subtle effects on the spectrum, automated classification in the luminosity domain has been somewhat more difficult, but still successful. In order to extend the use of automated techniques beyond a few surveys, we present our current efforts at building a web-based automated stellar spectroscopic classification and parameterization machine. Our proposed machinery would provide users with MK classifications as well as the astrophysical parameters of effective temperature, surface gravity, mean abundance, abundance anomalies, and microturbulence. 1. A BRIEF HISTORY OF AUTOMATED CLASSIFICATION Current or planned large-scale surveys, such as the Sloan Digital Sky Survey (York et al. 2000) or the GAIA mission (scheduled for launch around 2011), have led to increased interest in automated spectral classifiers (e.g., Bailer-Jones 2000). There are many other reasons to develop automated classifiers, not the least of which are the homogeneity of the results and the repeatability of the process. Automated spectral classification of stars goes back decades. For instance, in an early attempt Jones (1966) fit a few major stellar lines and correlated these indexes with MK type (Morgan, Keenan, & Kellman 1943). Malyuto & Shvelidze (1994) later developed this technique further. Unfortunately, the line fitting technique suffers from the disadvantage that one has first to know the approximate stellar type before determining which lines to fit, otherwise very different features will be found at the same wavelengths. Kurtz (1983; see also LaSala 1994) developed a minimum vector distance technique that matched spectra to a library of standards weighting the comparison to different spectral regions for different types of stars. The minimum vector distance technique has had some success – classifying stellar spectral types to within σ=2.2 spectral subtypes – but refining this technique is cumbersome since the weighting vectors need to be carefully established, yet they vary as a function of spectral type and luminosity class. The Garrison Festschrift Richard Gray, Christopher Corbally, & A. G. Davis Philip, eds. p. 1–-10 c © 2003 L. Davis Press 1 Ted von Hippel Dept. of Astronomy University of Texas