Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 514, Issue 1, Pages 1289-1301Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stac1458
Keywords
galaxies: statistics; cosmology: theory; (cosmology:) large-scale structure of Universe
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Funding
- Government of Canada through the Department of Innovation, Science and Economic Development Canada
- Province of Ontario through the Ministry of Colleges and Universities
- Canadian Space Agency (CSA)
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Compute Ontario
- Compute Canada
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We present correction terms that allow unbiased estimates of the data covariance matrix of the two-point correlation function to be recovered using delete-one Jackknife and Bootstrap methods. We demonstrate the accuracy and precision of this new method using a large set of QUIJOTE simulations and show that the corrected resampling techniques recover the correct amplitude and structure of the data covariance matrix.
We present correction terms that allow delete-one Jackknife and Bootstrap methods to be used to recover unbiased estimates of the data covariance matrix of the two-point correlation function xi(r). We demonstrate the accuracy and precision of this new method using a large set of 1000 QUIJOTE simulations that each cover a comoving volume of 1[h(-1)Gpc](3). The corrected resampling techniques recover the correct amplitude and structure of the data covariance matrix as represented by its principal components to within similar to 10 per cent, the level of error achievable with the size of the sample of simulations used for the test. Our corrections for the internal resampling methods are shown to be robust against the intrinsic clustering of the cosmological tracers both in real- and redshift space using two snapshots at z = 0 and z = 1 that mimic two samples with significantly different clustering. We also analyse two different slicing of the simulation volume into n(sv) = 64 or 125 sub-samples and show that the main impact of different n(sv) is on the structure of the covariance matrix due to the limited number of independent internal realizations that can be made given a fixed n(sv).
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