Abstract
Obtaining large samples of galaxy clusters is important for cosmology: cluster counts as a function of redshift and mass can constrain the parameters of our Universe. They are also useful in order to understand the formation and evolution of clusters. We develop an improved version of the Adami & MAzure Cluster FInder (AMACFI), now the Adami, MAzure & Sarron Cluster FInder (AMASCFI), and apply it to the 154 deg2 of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) to obtain a large catalogue of 1371 cluster candidates with mass M200 > 1014 M⊙ and redshift z ≤ 0.7. We derive the selection function of the algorithm from the Millennium simulation, and cluster masses from a richness–mass scaling relation built from matching our candidates with X-ray detections. We study the evolution of these clusters with mass and redshift by computing the i′-band galaxy luminosity functions (GLFs) for the early-type (ETGs) and late-type galaxies (LTGs). This sample is 90% pure and 70% complete, and therefore our results are representative of a large fraction of the cluster population in these redshift and mass ranges. We find an increase in both the ETG and LTG faint populations with decreasing redshift (with Schechter slopes αETG = −0.65 ± 0.03 and αLTG = −0.95 ± 0.04 at z = 0.6, and αETG = −0.79 ± 0.02 and αLTG = −1.26 ± 0.03 at z = 0.2) and also a decrease in the LTG (but not the ETG) bright end. Our large sample allows us to break the degeneracy between mass and redshift, finding that the redshift evolution is more pronounced in high-mass clusters, but that there is no significant dependence of the faint end on mass for a given redshift. These results show that the cluster red sequence is mainly formed at redshift z > 0.7, and that faint ETGs continue to enrich the red sequence through quenching of brighter LTGs at z ≤ 0.7. The efficiency of this quenching is higher in large-mass clusters, while the accretion rate of faint LTGs is lower as the more massive clusters have already emptied most of their environment at higher redshifts.
Highlights
As the most massive gravitationally bound structures in the universe, clusters of galaxies have been observed in great detail for decades
Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/IRFU, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii
When comparing the mass distributions obtained from the M200,WL − LX relations to that obtained by applying AMASCFI to the Millennium simulation, we find that the Leauthaud et al (2010) M200,WL − LX relation underpredicts the number of clusters with M200 > 1014 M, while the number of clusters predicted by the Kettula et al (2015) relation is in good agreement
Summary
As the most massive gravitationally bound structures in the universe, clusters of galaxies have been observed in great detail for decades. In addition to being interesting astrophysical objects, they are a powerful probe of cosmology since galaxy cluster counts as a function of mass and redshift depend on the cosmological parameters of our Universe Allen et al 2011, for a review) In this context it is important to obtain extensive samples of clusters covering wide redshift and mass ranges. The candidate cluster catalog is only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/613/A67 great precision, as the cosmological constraints are obtained by comparing the observed cluster counts to the predicted ones, either from the analytical halo mass function or from N-body simulations. The development of extended imaging surveys, such as the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS), provided the community with large sets of galaxy clusters observed homogeneously. The large sky coverage of these surveys demands an automated detection, and cluster detection algorithms are a hot topic in the literature, both for the present and generation surveys, such as that foreseen with Euclid, which will uncover hundreds of thousands of clusters
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