Improving Cosmological Constraints from Galaxy Cluster Number Counts with CMB-cluster-lensing Data: Results from the SPT-SZ Survey and Forecasts for the Future
Creators
- Chaubal, P. S.1
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Reichardt, C. L.1
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Gupta, N.1, 2
- Ansarinejad, B.1
- Aylor, K.3
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Balkenhol, L.1
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Baxter, E. J.4, 5
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Bianchini, F.6
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Benson, B. A.5, 7
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Bleem, L. E.5, 8
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Bocquet, S.9
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Carlstrom, J. E.5, 8
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Chang, C. L.5, 8
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Crawford, T. M.5
- Crites, A. T.5, 10
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de Haan, T.11, 12
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Dobbs, M. A.11, 13
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Everett, W. B.14
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Floyd, B.15
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George, E. M.12, 16
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Halverson, N. W.14
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Holzapfel, W. L.12
- Hrubes, J. D.5
- Knox, L.3
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Lee, A. T.12, 17
- Luong-Van, D.5
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McMahon, J. J.18
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Meyer, S. S.5
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Mocanu, L. M.5
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Mohr, J. J.9, 19, 20
- Natoli, T.5, 21
- Padin, S.5
- Pryke, C.22
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Ruhl, J. E.23
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Ruppin, F.24
- Salvati, L.25, 26, 27
- Saro, A.25, 26, 28, 29
- Schaffer, K. K.5, 30
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Shirokoff, E.5, 12
- Staniszewski, Z.K.23, 31
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Stark, A. A.32
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Vieira, J. D.33
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Williamson, R.5
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1.
University of Melbourne
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2.
Astronomy and Space
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3.
University of California, Davis
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4.
University of Pennsylvania
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5.
University of Chicago
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6.
Stanford University
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7.
Fermilab
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8.
Argonne National Laboratory
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9.
Ludwig-Maximilians-Universität München
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10.
California Institute of Technology
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11.
McGill University
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12.
University of California, Berkeley
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13.
Canadian Institute for Advanced Research
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14.
University of Colorado Boulder
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15.
University of Missouri–Kansas City
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16.
European Southern Observatory
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17.
Lawrence Berkeley National Laboratory
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18.
University of Michigan–Ann Arbor
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19.
Excellence Cluster Universe
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20.
Max Planck Institute for Extraterrestrial Physics
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21.
University of Toronto
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22.
University of Minnesota
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23.
Case Western Reserve University
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24.
Massachusetts Institute of Technology
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25.
Trieste Astronomical Observatory
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26.
Institute for Fundamental Physics of the Universe
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27.
French National Centre for Scientific Research
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28.
University of Trieste
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29.
INFN Sezione di Trieste
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30.
Art Institute of Chicago
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31.
Jet Propulsion Lab
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32.
Harvard-Smithsonian Center for Astrophysics
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33.
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).Attached Files
Published - Chaubal_2022_ApJ_931_139.pdf
Submitted - 2111.07491.pdf
Files
2111.07491.pdf
Additional details
Identifiers
- Eprint ID
- 115042
- Resolver ID
- CaltechAUTHORS:20220606-736249000
Related works
- Describes
- https://arxiv.org/abs/2111.07491 (URL)
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
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2022-06-07Created from EPrint's datestamp field
- Updated
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2022-06-07Created from EPrint's last_modified field