期刊
ASTROPHYSICAL JOURNAL
卷 763, 期 1, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/0004-637X/763/1/50
关键词
astronomical databases: miscellaneous; methods: data analysis; methods: statistical; stars: fundamental parameters; stars: general
资金
- Spanish Ministry for Science [AYA 2010-21887-C0404]
- Consolider-Ingenio Program [MICINN CSD2006-00070]
- Alfred P. Sloan Foundation
- National Science Foundation
- U.S. Department of Energy Office of Science
- University of Arizona
- Brazilian Participation Group
- Brookhaven National Laboratory
- University of Cambridge
- Carnegie Mellon University
- University of Florida
- French Participation Group
- German Participation Group
- Harvard University
- Instituto de Astrofisica de Canarias
- Michigan State/Notre Dame/JINA Participation Group
- Johns Hopkins University
- Lawrence Berkeley National Laboratory
- Max Planck Institute for Astrophysics
- Max Planck Institute for Extraterrestrial Physics
- New Mexico State University
- New York University
- Ohio State University
- Pennsylvania State University
- University of Portsmouth
- Princeton University
- Spanish Participation Group
- University of Tokyo
- University of Utah
- Vanderbilt University
- University of Virginia
- University of Washington
- Yale University
Large spectroscopic surveys require automated methods of analysis. This paper explores the use of k-means clustering as a tool for automated unsupervised classification of massive stellar spectral catalogs. The classification criteria are defined by the data and the algorithm, with no prior physical framework. We work with a representative set of stellar spectra associated with the Sloan Digital Sky Survey (SDSS) SEGUE and SEGUE-2 programs, which consists of 173,390 spectra from 3800 to 9200 angstrom sampled on 3849 wavelengths. We classify the original spectra as well as the spectra with the continuum removed. The second set only contains spectral lines, and it is less dependent on uncertainties of the flux calibration. The classification of the spectra with continuum renders 16 major classes. Roughly speaking, stars are split according to their colors, with enough finesse to distinguish dwarfs from giants of the same effective temperature, but with difficulties to separate stars with different metallicities. There are classes corresponding to particular MK types, intrinsically blue stars, dust-reddened, stellar systems, and also classes collecting faulty spectra. Overall, there is no one-to-one correspondence between the classes we derive and the MK types. The classification of spectra without continuum renders 13 classes, the color separation is not so sharp, but it distinguishes stars of the same effective temperature and different metallicities. Some classes thus obtained present a fairly small range of physical parameters (200 K in effective temperature, 0.25 dex in surface gravity, and 0.35 dex in metallicity), so that the classification can be used to estimate the main physical parameters of some stars at a minimum computational cost. We also analyze the outliers of the classification. Most of them turn out to be failures of the reduction pipeline, but there are also high redshift QSOs, multiple stellar systems, dust-reddened stars, galaxies, and, finally, odd spectra whose nature we have not deciphered. The template spectra representative of the classes are publicly available in the online journal and at ftp://stars:kmeans@ftp.iac.es.
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