Principal Component Analysis of SDSS Stellar Spectra

TL;DR

This paper applies PCA to a large set of SDSS stellar spectra, binning by color to account for non-linear variations, and provides eigenspectra and mean spectra useful for classification, synthesis, and analysis of stellar properties.

Contribution

The study introduces a PCA-based method applied to SDSS stellar spectra within color bins, providing eigenspectra and mean spectra that facilitate spectral analysis and classification.

Findings

Four eigenspectra suffice to describe spectra within measurement noise

Eigencoefficients correlate with metallicity and gravity

Publicly available spectra and eigenspectra enable diverse applications

Abstract

We apply Principal Component Analysis (PCA) to ~100,000 stellar spectra obtained by the Sloan Digital Sky Survey (SDSS). In order to avoid strong non-linear 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 to 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 (SEGUE) 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.