A cluster finding algorithm based on the multiband identification of red sequence galaxies

Abstract

We present a new algorithm, CAMIRA, to identify clusters of galaxies in wide-field imaging survey data. We base our algorithm on the stellar population synthesis model to predict colours of red-sequence galaxies at a given redshift for an arbitrary set of bandpass filters, with additional calibration using a sample of spectroscopic galaxies to improve the accuracy of the model prediction. We run the algorithm on ~11960 deg^2 of imaging data from the Sloan Digital Sky Survey (SDSS) Data Release 8 to construct a catalogue of 71743 clusters in the redshift range 0.1<z<0.6 with richness after correcting for the incompleteness of the richness estimate greater than 20. We cross-match the cluster catalogue with external cluster catalogues to find that our photometric cluster redshift estimates are accurate with low bias and scatter, and that the corrected richness correlates well with X-ray luminosities and temperatures. We use the publicly available Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS) shear catalogue to calibrate the mass-richness relation from stacked weak lensing analysis. Stacked weak lensing signals are detected significantly for 8 subsamples of the SDSS clusters divided by redshift and richness bins, which are then compared with model predictions including miscentring effects to constrain mean halo masses of individual bins. We find the richness correlates well with the halo mass, such that the corrected richness limit of 20 corresponds to the cluster virial mass limit of about 1 \times 10^14 M_Sun/h for the SDSS DR8 cluster sample.