Title: RedMapper and RedMagic: Enabling Cluster and LSS Science in Photometric Surveys
Photometric identification of galaxy clusters has long had a reputation for being notoriously noisy. Similarly, photometric redshift uncertainties are now recognized as a critical source of systematic uncertainty for LSS photometric studies. We present redMaPPer and redMaGiC, two algorithms specifically designed to address these difficulties, and illustrate their performance on the SDSS and DES data sets. We then discuss specific examples of the kind of research enabled by these samples, including the first detection of assembly bias, and the first high quality mass calibration of galaxy clusters from cluster clustering.