Optimizing Color Selection for Cluster-Member Identification with SuperBIT
Galaxy richness maps provide a useful tracer of the stellar mass distribution in galaxy clusters, but identifying cluster members from multiband photometric data can be challenging. In this work, we investigate multiband imaging data obtained with the Super-Pressure Balloon-borne Imaging Telescope (SuperBIT) during its science flight in April 2023. Using spectroscopically identified cluster members across the SuperBIT cluster sample, we benchmark the performance of the three colors available from the three SuperBIT bands: $(u-b)$, $(b-g)$, and $(u-g)$. We quantify the performance of each color using the $1\,σ$ scatter of the spectroscopically identified members in color and the purity of our cluster-member identification algorithm. We find that $(b-g)$ consistently exhibits the smallest color scatter and highest purity across the sample and over the redshift range probed by SuperBIT. We therefore identify $(b-g)$ as the preferred color for red-sequence-based cluster-member identification, providing a basis for future analyses of the cluster galaxy population using SuperBIT multiband photometry.