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Cited by 338

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Cosmic Complementarity: Joint Parameter Estimation from Cosmic Microwave Background Experiments and Redshift Surveys

Open access
Eisenstein, Daniel J., Hu, Wayne, Tegmark, Max·1999·The Astrophysical Journal

Abstract

We study the ability of future cosmic microwave background anisotropy experiments and redshift surveys to constrain a 13-dimensional parameterization of the adiabatic cold dark matter model. Each alone is unable to determine all parameters to high accuracy. However, considered together, one data set resolves the difficulties of the other, allowing certain degenerate parameters to be determined with far greater precision. We treat in detail the degeneracies involving the classical cosmological parameters, massive neutrinos, tensor-scalar ratio, bias, and reionization optical depth as well as how redshift surveys can resolve them. We discuss the opportunities for internal and external consistency checks on these measurements. Previous papers on parameter estimation have generally treated smaller parameter spaces; in direct comparisons to these works, we tend to find weaker constraints and suggest numerical explanations for the discrepancies.

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Metadata

Authors
Eisenstein, D. J., Hu, W., & Tegmark, M.
Venue
The Astrophysical Journal
Published
1999-06-10
Volume
518
Issue
1
Pages
2-23
ISSN
  • 0004-637X (ISSN-L)
  • 1538-4357
OpenAlex
W1498004781
Cited by
338
References
3
FWCI
29.51
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Cosmic Complementarity: Joint Parameter Estimation from Cosmic Microwave Background Experiments and Redshift Surveys. https://app.cochat.ai/papers/10.1086/307261
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Eisenstein, D. J., Hu, W., & Tegmark, M. (1999). Cosmic Complementarity: Joint Parameter Estimation from Cosmic Microwave Background Experiments and Redshift Surveys. The Astrophysical Journal. https://doi.org/10.1086/307261

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.