Red Dragon: A Redshift-Evolving Gaussian Mixture Model for Galaxies

Precision-era optical cluster cosmology calls for a precise definition of the red sequence (RS), consistent across redshift. To this end, we present the Red Dragon algorithm: an error-corrected multivariate Gaussian mixture model (GMM). Simultaneous use of multiple colors and smooth evolution of GMM parameters result in a continuous RS and blue cloud (BC) characterization across redshift, avoiding the discontinuities of red fraction inherent in swapping RS selection colors. Based on a mid-redshift spectroscopic sample of SDSS galaxies, a RS defined by Red Dragon selects quiescent galaxies (low specific star formation rate) with a balanced accuracy of over $90\%$. This approach to galaxy population assignment gives more natural separations between RS and BC galaxies than hard cuts in color–magnitude or color–color spaces. The Red Dragon algorithm is publicly available at https://bitbucket.org/wkblack/red-dragon-gamma.