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Extracting Primordial Non-Gaussianity without Cosmic Variance
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Recent work has emphasized the possibility to probe non-Gaussianity of local type by measuring the power spectrum of highly biased tracers of large scale structure on very large scales. This method is limited by the cosmic variance, by the finite number of structures on the largest scales, and by the partial degeneracy with other cosmological parameters that can mimic the same effect. We propose an alternative method based on the fact that on large scales, halos are linearly biased, but not stochastic, tracers of dark matter: by correlating a highly biased tracer of large scale structure against an unbiased tracer, one eliminates the cosmic variance error, which can lead to a significant increase in signal to noise. For an ideal survey out to z approximately 2, the error reduction can be as large as a factor of 7, which should guarantee a detection of non-Gaussianity from an all-sky survey of this type.
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“Although measurements of the galaxy power spectrum on very large scales are challenging owing to sky systematics ( Leistedt et al. 2014 ) and cosmic variance, the prospect of using multiple tracers of the same fi eld could signi fi cantly improve the constraining power for this observable ( Seljak 2009 ) .”
From LSST: From Science Drivers to Reference Design and Anticipated Data Products (2019) · cites this paper as Seljak 2009
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Green, D., Guo, Y., Han, J., & Wallisch, B. (2024) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2024/05/090 · Cited by 22
Curto, A., Martínez-González, E., Barreiro, R. B., & Hobson, M. (2011) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2011.19284.x · Cited by 20
Density reconstruction from biased tracers and its application to primordial non-Gaussianity
Darwish, O., Foreman, S., Abidi, M. M., Baldauf, T., Sherwin, B. D., & Meerburg, P. D. (2021) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.104.123520 · Cited by 20
Taming assembly bias for primordial non-Gaussianity
Fondi, E., Verde, L., Villaescusa-Navarro, F., Baldi, M., Coulton, W. R., Jung, G., Karagiannis, D., Liguori, M., Ravenni, A., & Wandelt, B. D. (2024) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2024/02/048 · Cited by 20
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Relativistic effects in Lyman-α forest
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Metadata
- Authors
- Seljak, U.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Seljak, Uroš
- ORCID
- Seljak, Uroš
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2009-01-15
- Volume
- 102
- Issue
- 2
- Pages
- 021302-021302
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2009350306
- Cited by
- 354
- References
- 22
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 24 · 2023: 18 · 2022: 26 · 2021: 27 · 2020: 31 · 2019: 36 · 2018: 20 · 2017: 15 · 2016: 24 · 2015: 19 · 2014: 14 · 2013: 11 · 2012: 14
- FWCI
- 9.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Zurich Open Repository and Archive (University of Zurich)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Dark Matter and Cosmic Phenomena
- Keywords
- primordial non-Gaussianity, cosmic variance, local non-Gaussianity, large-scale structure, biased tracers, dark matter halos, halo bias, cross correlation function, power spectrum, signal-to-noise ratio, all-sky survey
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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