Precision Measurements of the Cluster Red Sequence using an Error Corrected Gaussian Mixture Model

TL;DR

This paper introduces an error corrected Gaussian Mixture Model to accurately identify red sequence galaxies in galaxy clusters, enabling precise measurements of their properties and insights into galaxy evolution over redshift.

Contribution

The paper presents a novel error correction technique for Gaussian Mixture Models to unbiasedly measure red sequence properties in galaxy clusters.

Findings

Red sequence scatter increases mildly with redshift.

Red sequence slope decreases with redshift.

Ridgeline properties are environment-independent.

Abstract

The red sequence is an important feature of galaxy clusters and plays a crucial role in optical cluster detection. Measurement of the slope and scatter of the red sequence are affected both by selection of red sequence galaxies and measurement errors. In this paper, we describe a new error corrected Gaussian Mixture Model for red sequence galaxy identification. Using this technique, we can remove the effects of measurement error and extract unbiased information about the intrinsic properties of the red sequence. We use this method to select red sequence galaxies in each of the 13,823 clusters in the maxBCG catalog, and measure the red sequence ridgeline location and scatter of each. These measurements provide precise constraints on the variation of the average red galaxy populations in the observed frame with redshift. We find that the scatter of the red sequence ridgeline increases mildly with redshift, and that the slope decreases with redshift. We also observe that the slope does not strongly depend on cluster richness. Using similar methods, we show that this behavior is mirrored in a spectroscopic sample of field galaxies, further emphasizing that ridgeline properties are independent of environment.