SPT clusters with DES and HST weak lensing. I. Cluster lensing and Bayesian population modeling of multiwavelength cluster datasets

Bocquet, S. (ORCID:000000024900805X); Grandis, S.; Bleem, L. E.; Klein, M.; Mohr, J. J.; Aguena, M.; Alarcon, A.; Allam, S.; Allen, S. W.; Alves, O.; Amon, A.; Ansarinejad, B.; Bacon, D.; Bayliss, M.; Bechtol, K.; Becker, M. R.; Benson, B. A.; Bernstein, G. M.; Brodwin, M.; Brooks, D.; Campos, A.; Canning, R. E. A.; Carlstrom, J. E.; Carnero_Rosell, A.; Carrasco_Kind, M.; Carretero, J.; Cawthon, R.; Chang, C.; Chen, R.; Choi, A.; Cordero, J.; Costanzi, M.; da_Costa, L. N.; Pereira, M. E. S.; Davis, C.; DeRose, J.; Desai, S.; de_Haan, T.; De_Vicente, J.; Diehl, H. T.; Dodelson, S.; Doel, P.; Doux, C.; Drlica-Wagner, A.; Eckert, K.; Elvin-Poole, J.; Everett, S.; Ferrero, I.; Ferté, A.; Flores, A. M.; Frieman, J.; García-Bellido, J.; Gatti, M.; Giannini, G.; Gladders, M. D.; Gruen, D.; Gruendl, R. A.; Harrison, I.; Hartley, W. G.; Herner, K.; Hinton, S. R.; Hollowood, D. L.; Holzapfel, W. L.; Honscheid, K.; Huang, N.; Huff, E. M.; James, D. J.; Jarvis, M.; Khullar, G.; Kim, K.; Kraft, R.; Kuehn, K.; Kuropatkin, N.; Kéruzoré, F.; Lee, S.; Leget, P-F; MacCrann, N.; Mahler, G.; Mantz, A.; Marshall, J. L.; McCullough, J.; McDonald, M.; Mena-Fernández, J.; Miquel, R.; Myles, J.; Navarro-Alsina, A.; Ogando, R. L. C.; Palmese, A.; Pandey, S.; Pieres, A.; Plazas_Malagón, A. A.; Prat, J.; Raveri, M.; Reichardt, C. L.; Roberson, J.; Rollins, R. P.; Romer, A. K.; Romero, C.; Roodman, A.; Ross, A. J.; Rykoff, E. S.; Salvati, L.; Sánchez, C.; Sanchez, E.; Sanchez_Cid, D.; Saro, A.; Schrabback, T.; Schubnell, M.; Secco, L. F.; Sevilla-Noarbe, I.; Sharon, K.; Sheldon, E.; Shin, T.; Smith, M.; Somboonpanyakul, T.; Stalder, B.; Stark, A. A.; Strazzullo, V.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; To, C.; Troxel, M. A.; Tutusaus, I.; Varga, T. N.; von_der_Linden, A.; Weaverdyck, N.; Weller, J.; Wiseman, P.; Yanny, B.; Yin, B.; Young, M.; Zhang, Y.; Zuntz, J.; The_DES_and_SPT_Collaborations

doi:10.1103/PhysRevD.110.083509

ABSTRACT Cosmological constraints from current and upcoming galaxy cluster surveys are limited by the accuracy of cluster mass calibration. In particular, optically identified galaxy clusters are prone to selection effects that can bias the weak lensing mass calibration. We investigate the selection bias of the stacked cluster lensing signal associated with optically selected clusters, using clusters identified by the redMaPPer algorithm in the Buzzard simulations as a case study. We find that at a given cluster halo mass, the residuals of redMaPPer richness and weak lensing signal are positively correlated. As a result, for a given richness selection, the stacked lensing signal is biased high compared with what we would expect from the underlying halo mass probability distribution. The cluster lensing selection bias can thus lead to overestimated mean cluster mass and biased cosmology results. We show that the lensing selection bias exhibits a strong scale dependence and is approximately 20–60 per cent for ΔΣ at large scales. This selection bias largely originates from spurious member galaxies within ±20–60 $$h^{-1}\, \rm Mpc$$ along the line of sight, highlighting the importance of quantifying projection effects associated with the broad redshift distribution of member galaxies in photometric cluster surveys. While our results qualitatively agree with those in the literature, accurate quantitative modelling of the selection bias is needed to achieve the goals of cluster lensing cosmology and will require synthetic catalogues covering a wide range of galaxy–halo connection models.

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