Journal article
Constraining Cluster Virialization Mechanism and Cosmology Using Thermal-SZ-selected Clusters from Future CMB Surveys
S Raghunathan, N Whitehorn, MA Alvarez, H Aung, N Battaglia, GP Holder, D Nagai, E Pierpaoli, CL Reichardt, JD Vieira
Astrophysical Journal | Published : 2022
Abstract
We forecast the number of galaxy clusters that can be detected via the thermal Sunyaev-Zel'dovich (tSZ) signals by future cosmic microwave background (CMB) experiments, primarily the wide area survey of the CMB-S4 experiment but also CMB-S4's smaller de-lensing survey and the proposed CMB-HD experiment. We predict that CMB-S4 will detect 75,000 clusters with its wide survey of f sky = 50% and 14,000 clusters with its deep survey of f sky = 3%. Of these, approximately 1350 clusters will be at z ≥ 2, a regime that is difficult to probe by optical or X-ray surveys. We assume CMB-HD will survey the same sky as the S4-Wide, and find that CMB-HD will detect three times more overall and an order of..
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Awarded by National Aeronautics and Space Administration
Funding Acknowledgements
We thank the entire CMB-S4 collaboration<SUP>17</SUP> for helpful comments and suggestions throughout the course of this work. We further thank Neelima Sehgal for feedback on the manuscript; Sebastian Bocquet and Nikhel Gupta for useful discussions; and Yuuki Omori for providing access to MDPL2 simulations. Finally, we thank the anonymous referee for the useful suggestions that helped in shaping this manuscript better. S.R. is supported by the Illinois Survey Science Fellowship from the Center for AstroPhysical Surveys at the National Center for Supercomputing Applications. S.R. and N. W. acknowledge support from NSF grants AST-1716965 and CSSI-1835865. S.R., N.W., G.H., and J.V. acknowledge support from NSF grant OPP-1852617. D.N. and H.A. acknowledge support from the facilities and staff of the Yale Center for Research Computing. N.B. acknowledges support from NSF grant AST-1910021. E.P. is supported by NASA grant 80NSSC18K0403 and the Simons Foundation award No. 615662, as well as NSF grant AST-1910678. C.R. acknowledges support from the Australian Research Council's Discovery Projects scheme (DP200101068). J.V. acknowledges support from NSF under grants AST-1715213 and AST1716127. This work used computational and storage services associated with the Hoffman2 Shared Cluster provided by UCLA Institute for Digital Research and Education's Research Technology Group.