Disconnected pseudo-Cℓ covariances for projected large-scale structure data
The disconnected part of the power spectrum covariance matrix (also known as the "Gaussian" covariance) is the dominant contribution on large scales for galaxy clustering and weak lensing datasets. The presence of a complicated sky mask causes non-trivial correlations between different Fou...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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IOP Publishing
2019
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| _version_ | 1797098307936321536 |
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| author | García-García, C Alonso, D Bellini, E |
| author_facet | García-García, C Alonso, D Bellini, E |
| author_sort | García-García, C |
| collection | OXFORD |
| description | The disconnected part of the power spectrum covariance matrix (also known as the "Gaussian" covariance) is the dominant contribution on large scales for galaxy clustering and weak lensing datasets. The presence of a complicated sky mask causes non-trivial correlations between different Fourier/harmonic modes, which must be accurately characterized in order to obtain reliable cosmological constraints. This is particularly relevant for galaxy survey data. Unfortunately, an exact calculation of these correlations involves O(ℓmax6) operations that become computationally impractical very quickly. We present an implementation of approximate methods to estimate the Gaussian covariance matrix of power spectra involving spin-0 and spin-2 flat- and curved-sky fields, expanding on existing algorithms {developed in the context of CMB analyses}. These methods achieve an O(ℓmax3) scaling, which makes the computation of the covariance matrix as fast as the computation of the power spectrum itself. We quantify the accuracy of these methods on large-scale structure and weak lensing data, making use of a large number of Gaussian but otherwise realistic simulations. We show that, using the approximate covariance matrix, we are able to recover the true posterior distribution of cosmological parameters to high accuracy. We also quantify the shortcomings of these methods, which become unreliable on the very largest scales, as well as for covariance matrix elements involving cosmic shear B modes. The algorithms presented here are implemented in the public code NaMaster https://github.com/LSSTDESC/NaMaster. |
| first_indexed | 2024-03-07T05:07:38Z |
| format | Journal article |
| id | oxford-uuid:da784af1-8ee8-4370-a5c1-2cc582c6687a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:07:38Z |
| publishDate | 2019 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | oxford-uuid:da784af1-8ee8-4370-a5c1-2cc582c6687a2022-03-27T09:03:28ZDisconnected pseudo-Cℓ covariances for projected large-scale structure dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:da784af1-8ee8-4370-a5c1-2cc582c6687aEnglishSymplectic Elements at OxfordIOP Publishing2019García-García, CAlonso, DBellini, EThe disconnected part of the power spectrum covariance matrix (also known as the "Gaussian" covariance) is the dominant contribution on large scales for galaxy clustering and weak lensing datasets. The presence of a complicated sky mask causes non-trivial correlations between different Fourier/harmonic modes, which must be accurately characterized in order to obtain reliable cosmological constraints. This is particularly relevant for galaxy survey data. Unfortunately, an exact calculation of these correlations involves O(ℓmax6) operations that become computationally impractical very quickly. We present an implementation of approximate methods to estimate the Gaussian covariance matrix of power spectra involving spin-0 and spin-2 flat- and curved-sky fields, expanding on existing algorithms {developed in the context of CMB analyses}. These methods achieve an O(ℓmax3) scaling, which makes the computation of the covariance matrix as fast as the computation of the power spectrum itself. We quantify the accuracy of these methods on large-scale structure and weak lensing data, making use of a large number of Gaussian but otherwise realistic simulations. We show that, using the approximate covariance matrix, we are able to recover the true posterior distribution of cosmological parameters to high accuracy. We also quantify the shortcomings of these methods, which become unreliable on the very largest scales, as well as for covariance matrix elements involving cosmic shear B modes. The algorithms presented here are implemented in the public code NaMaster https://github.com/LSSTDESC/NaMaster. |
| spellingShingle | García-García, C Alonso, D Bellini, E Disconnected pseudo-Cℓ covariances for projected large-scale structure data |
| title | Disconnected pseudo-Cℓ covariances for projected large-scale structure data |
| title_full | Disconnected pseudo-Cℓ covariances for projected large-scale structure data |
| title_fullStr | Disconnected pseudo-Cℓ covariances for projected large-scale structure data |
| title_full_unstemmed | Disconnected pseudo-Cℓ covariances for projected large-scale structure data |
| title_short | Disconnected pseudo-Cℓ covariances for projected large-scale structure data |
| title_sort | disconnected pseudo cl covariances for projected large scale structure data |
| work_keys_str_mv | AT garciagarciac disconnectedpseudoclcovariancesforprojectedlargescalestructuredata AT alonsod disconnectedpseudoclcovariancesforprojectedlargescalestructuredata AT bellinie disconnectedpseudoclcovariancesforprojectedlargescalestructuredata |