TL;DR
Red Dragon introduces an error-corrected Gaussian mixture model that accurately characterizes galaxy populations across redshift, improving the definition of the red sequence for cosmological studies.
Contribution
It presents a novel multivariate GMM approach with smooth redshift evolution for defining galaxy populations, addressing discontinuities in traditional methods.
Findings
Achieves over 90% accuracy in selecting quenched galaxies.
Provides a continuous and natural separation of galaxy populations.
Avoids redshift-dependent discontinuities in galaxy classification.
Abstract
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…
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