Published June 1, 2022 | Version Published + Submitted
Journal Article Open

Improving Cosmological Constraints from Galaxy Cluster Number Counts with CMB-cluster-lensing Data: Results from the SPT-SZ Survey and Forecasts for the Future

  • 1. ROR icon University of Melbourne
  • 2. ROR icon Astronomy and Space
  • 3. ROR icon University of California, Davis
  • 4. ROR icon University of Pennsylvania
  • 5. ROR icon University of Chicago
  • 6. ROR icon Stanford University
  • 7. ROR icon Fermilab
  • 8. ROR icon Argonne National Laboratory
  • 9. ROR icon Ludwig-Maximilians-Universität München
  • 10. ROR icon California Institute of Technology
  • 11. ROR icon McGill University
  • 12. ROR icon University of California, Berkeley
  • 13. ROR icon Canadian Institute for Advanced Research
  • 14. ROR icon University of Colorado Boulder
  • 15. ROR icon University of Missouri–Kansas City
  • 16. ROR icon European Southern Observatory
  • 17. ROR icon Lawrence Berkeley National Laboratory
  • 18. ROR icon University of Michigan–Ann Arbor
  • 19. ROR icon Excellence Cluster Universe
  • 20. ROR icon Max Planck Institute for Extraterrestrial Physics
  • 21. ROR icon University of Toronto
  • 22. ROR icon University of Minnesota
  • 23. ROR icon Case Western Reserve University
  • 24. ROR icon Massachusetts Institute of Technology
  • 25. ROR icon Trieste Astronomical Observatory
  • 26. ROR icon Institute for Fundamental Physics of the Universe
  • 27. ROR icon French National Centre for Scientific Research
  • 28. ROR icon University of Trieste
  • 29. ROR icon INFN Sezione di Trieste
  • 30. ROR icon Art Institute of Chicago
  • 31. ROR icon Jet Propulsion Lab
  • 32. ROR icon Harvard-Smithsonian Center for Astrophysics
  • 33. ROR icon University of Illinois Urbana-Champaign

Abstract

We show the improvement to cosmological constraints from galaxy cluster surveys with the addition of cosmic microwave background (CMB)-cluster lensing data. We explore the cosmological implications of adding mass information from the 3.1σ detection of gravitational lensing of the CMB by galaxy clusters to the Sunyaev–Zel'dovich (SZ) selected galaxy cluster sample from the 2500 deg² SPT-SZ survey and targeted optical and X-ray follow-up data. In the ΛCDM model, the combination of the cluster sample with the Planck power spectrum measurements prefers σ₈(Ωₘ/0.3)^(0.5} = 0.831 ± 0.020. Adding the cluster data reduces the uncertainty on this quantity by a factor of 1.4, which is unchanged whether the 3.1σ CMB-cluster lensing measurement is included or not. We then forecast the impact of CMB-cluster lensing measurements with future cluster catalogs. Adding CMB-cluster lensing measurements to the SZ cluster catalog of the ongoing SPT-3G survey is expected to improve the expected constraint on the dark energy equation of state w by a factor of 1.3 to σ(w) = 0.19. We find the largest improvements from CMB-cluster lensing measurements to be for σ₈, where adding CMB-cluster lensing data to the cluster number counts reduces the expected uncertainty on σ₈ by respective factors of 2.4 and 3.6 for SPT-3G and CMB-S4.

Additional Information

© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 November 14; revised 2022 April 20; accepted 2022 April 24; published 2022 June 3. The South Pole Telescope program is supported by the National Science Foundation (NSF) through award OPP-1852617. Argonne National Laboratory's work was supported by the U.S. Department of Energy, Office of High Energy Physics, under contract DE-AC02-06CH11357. We also acknowledge support from the Argonne Center for Nanoscale Materials. The Melbourne group acknowledges support from the Australian Research Council's Discovery Projects scheme (DP200101068). A.A.S. acknowledges support by U.S. National Science Foundation grant AST-1814719. A.S. is supported by the FARE-MIUR grant "ClustersXEuclid" R165SBKTMA, INFN InDark, and by the ERC-StG "ClustersXCosmo" grant agreement 716762. The data analysis pipeline also uses the scientific Python stack (Jones et al. 2001; Hunter 2007; van der Walt et al. 2011). We acknowledge the use of the Spartan, a high performance computing facility at the University of Melbourne (Lafayette et al. 2016).

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Published - Chaubal_2022_ApJ_931_139.pdf

Submitted - 2111.07491.pdf

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Additional details

Identifiers

Eprint ID
115042
Resolver ID
CaltechAUTHORS:20220606-736249000

Related works

Funding

NSF
OPP-1852617
Department of Energy (DOE)
DE-AC02- 06CH11357
Australian Research Council
DP200101068
NSF
AST-1814719
Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR)
R165SBKTMA
Istituto Nazionale di Fisica Nucleare (INFN)
European Research Council (ERC)
716762

Dates

Created
2022-06-07
Created from EPrint's datestamp field
Updated
2022-06-07
Created from EPrint's last_modified field