Journal article
Cosmic shear power spectra in practice
- Abstract:
- Cosmic shear is one of the most powerful probes of Dark Energy, targeted by several current and future galaxy surveys. Lensing shear, however, is only sampled at the positions of galaxies with measured shapes in the catalog, making its associated sky window function one of the most complicated amongst all projected cosmological probes of inhomogeneities, as well as giving rise to inhomogeneous noise. Partly for this reason, cosmic shear analyses have been mostly carried out in real-space, making use of correlation functions, as opposed to Fourier-space power spectra. Since the use of power spectra can yield complementary information and has numerical advantages over real-space pipelines, it is important to develop a complete formalism describing the standard unbiased power spectrum estimators as well as their associated uncertainties. Building on previous work, this paper contains a study of the main complications associated with estimating and interpreting shear power spectra, and presents fast and accurate methods to estimate two key quantities needed for their practical usage: the noise bias and the Gaussian covariance matrix, fully accounting for survey geometry, with some of these results also applicable to other cosmological probes. We demonstrate the performance of these methods by applying them to the latest public data releases of the Hyper Suprime-Cam and the Dark Energy Survey collaborations, quantifying the presence of systematics in our measurements and the validity of the covariance matrix estimate. We make the resulting power spectra, covariance matrices, null tests and all associated data necessary for a full cosmological analysis publicly available.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, 3.3MB, Terms of use)
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- Publisher copy:
- 10.1088/1475-7516/2021/03/067
Authors
- Publisher:
- IOP Publishing
- Journal:
- Journal of Cosmology and Astroparticle Physics More from this journal
- Volume:
- 2021
- Issue:
- 3
- Article number:
- 067
- Publication date:
- 2021-03-22
- Acceptance date:
- 2021-01-29
- DOI:
- EISSN:
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1475-7516
- Language:
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English
- Keywords:
- Pubs id:
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1139057
- Local pid:
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pubs:1139057
- Deposit date:
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2021-04-21
Terms of use
- Copyright holder:
- IOP Publishing and Sissa Medialab.
- Copyright date:
- 2021
- Rights statement:
- © 2021 IOP Publishing Ltd and Sissa Medialab
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from IOP Science at: http://dx.doi.org/10.1088/1475-7516/2021/03/067
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