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 quenched 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 bitbucket.org/wkblack/red-dragon-gamma.
