Apples to apples A^2 – II. Cluster selection functions for next-generation surveys
We present the cluster selection function for three of the largest next-generation stage-IV surveys in the optical and infrared: Euclid-Optimistic, Euclid-Pessimistic and the Large Synoptic Survey Telescope (LSST). To simulate these surveys, we use the realistic mock catalogues introduced in the first paper of this series. We detected galaxy clusters using the Bayesian Cluster Finder in the mock catalogues. We then modelled and calibrated the total cluster stellar mass observable–theoretical mass (M^∗_(CL)—M_h) relation using a power-law model, including a possible redshift evolution term. We find a moderate scatter of σM^∗_(CL)|M_h) of 0.124, 0.135 and 0.136 dex for Euclid-Optimistic, Euclid-Pessimistic and LSST, respectively, comparable to other work over more limited ranges of redshift. Moreover, the three data sets are consistent with negligible evolution with redshift, in agreement with observational and simulation results in the literature. We find that Euclid-Optimistic will be able to detect clusters with >80 per cent completeness and purity down to 8 × 10^(13) h^(−1) M_⊙ up to z < 1. At higher redshifts, the same completeness and purity are obtained with the larger mass threshold of 2 × 10^(14) h^(−1) M_⊙ up to z = 2. The Euclid-Pessimistic selection function has a similar shape with ∼10 per cent higher mass limit. LSST shows ∼5 per cent higher mass limit than Euclid-Optimistic up to z < 0.7 and increases afterwards, reaching a value of 2 × 10^(14) h^(−1) M_⊙ at z = 1.4. Similar selection functions with only 80 per cent completeness threshold have also been computed. The complementarity of these results with selection functions for surveys in other bands is discussed.
© 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. Received: 24 May 2016. Revision Received: 28 September 2016. Accepted: 30 September 2016. Published: 03 October 2016. We acknowledge the anonymous referee for providing useful comments that helped improving this manuscript. BA thanks Thomas Reiprich and Annalisa Pillepich, who kindly provided the updated e-ROSITA selection function curves. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 656354 and support from a postdoctoral fellowship at the Observatory of Paris. SM acknowledges financial support from the Institut Universitaire de France (IUF), of which she is senior member. We thank Peter Schneider and Andrea Biviano for useful comments on the manuscript.
Submitted - 1605.07620.pdf
Published - stw2508.pdf