2010/01/26 by Rosalie McGurk, Rosalie C. McGurk, Amy Kimball +3 · 39 citations
Physics and Astronomy · #Algorithm #Artificial intelligence #Astronomical spectroscopy #Astronomy #Astronomy and Astrophysical Research #Astrophysics #Bin #Computer science #Galaxies: Formation, Evolution, Phenomena #Metallicity #Physics #Principal component analysis #Sky #Spectral line #Stars #Stellar classification #Stellar, planetary, and galactic studies #Surface gravity #astro-ph.GA #astro-ph.SR
paper · pdf · doi:10.1088/0004-6256/139/3/1261
published in The Astronomical Journal 139(3), 1261-1268 (Institute of Physics) · 25 pages, 15 figures, accepted by the Astronomical Journal
arxiv created 2010/01/26 · openalex publication_date 2010/02/11 · arxiv updated 2010/02/26 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We apply Principal Component Analysis (PCA) to ∼100,000 stellar spectra obtained by the Sloan Digital Sky Survey (SDSS). In order to avoid strong nonlinear variation of spectra with effective temperature, the sample is binned into 0.02 mag wide intervals of the g − r color (−0.20 < g − r < 0.90, roughly corresponding to MK spectral types A3–K3), and PCA is applied independently for each bin. In each color bin, the first four eigenspectra are sufficient to describe the observed spectra within the measurement noise. We discuss correlations of eigencoefficients with metallicity and gravity estimated by the Sloan Extension for Galactic Understanding and Exploration Stellar Parameters Pipeline. The resulting high signal-to-noise mean spectra and the other three eigenspectra are made publicly available. These data can be used to generate high-quality spectra for an arbitrary combination of effective temperature, metallicity, and gravity within the parameter space probed by the SDSS. The SDSS stellar spectroscopic database and the PCA results presented here offer a convenient method to classify new spectra, to search for unusual spectra, to train various spectral classification methods, and to synthesize accurate colors in arbitrary optical bandpasses.