Add to ORKG11 pages, 11 figures, 2 tablesWe present a fast and robust alternative method to compute covariance matrix in case of cosmology studies. Our method is based on the jackknife resampling applied on simulation mock catalogues. Using a set of 600 BOSS DR11 mock catalogues as a reference, we find that the jackknife technique gives a similar galaxy clustering covariance matrix estimate by requiring a smaller number of mocks. A comparison of convergence rates show that $\sim$7 times fewer simulations are needed to get a similar accuracy on variance. We expect this technique to be applied in any analysis where the number of available N-body simulations is low
Cosmological covariance matrices are fundamental for parameter inference, since they are responsible...
International audienceWe compare the measurements of the bispectrum and the estimate of its covarian...
Accepted —. Received —;in original form — For galaxy clustering to provide robust constraints on cos...
11 pages, 11 figures, 2 tablesWe present a fast and robust alternative method to compute covariance ...
International audienceWe present an approach for accurate estimation of the covariance of 2-point co...
To make use of clustering statistics from large cosmological surveys, accurate and precise covarianc...
International audienceThis paper is the first in a set that analyses the covariance matrices of clus...
We present FITCOV an approach for accurate estimation of the covariance of two-point correlation fun...
We present correction terms that allow delete-one Jackknife and Bootstrap methods to be used to reco...
This paper is the first in a set that analyses the covariance matrices of clustering statistics obta...
The jackknife method gives an internal covariance estimate for large-scale structure surveys and all...
Citation: Pearson, D. W., & Samushia, L. (2016). Estimating the power spectrum covariance matrix wit...
We perform a detailed analysis of the covariance matrix of the spherically averaged galaxy power spe...
International audienceThe covariance matrix Σ of non-linear clustering statistics that are measured ...
Data analysis in cosmology requires reliable covariance matrices. Covariance matrices derived from n...
Cosmological covariance matrices are fundamental for parameter inference, since they are responsible...
International audienceWe compare the measurements of the bispectrum and the estimate of its covarian...
Accepted —. Received —;in original form — For galaxy clustering to provide robust constraints on cos...
11 pages, 11 figures, 2 tablesWe present a fast and robust alternative method to compute covariance ...
International audienceWe present an approach for accurate estimation of the covariance of 2-point co...
To make use of clustering statistics from large cosmological surveys, accurate and precise covarianc...
International audienceThis paper is the first in a set that analyses the covariance matrices of clus...
We present FITCOV an approach for accurate estimation of the covariance of two-point correlation fun...
We present correction terms that allow delete-one Jackknife and Bootstrap methods to be used to reco...
This paper is the first in a set that analyses the covariance matrices of clustering statistics obta...
The jackknife method gives an internal covariance estimate for large-scale structure surveys and all...
Citation: Pearson, D. W., & Samushia, L. (2016). Estimating the power spectrum covariance matrix wit...
We perform a detailed analysis of the covariance matrix of the spherically averaged galaxy power spe...
International audienceThe covariance matrix Σ of non-linear clustering statistics that are measured ...
Data analysis in cosmology requires reliable covariance matrices. Covariance matrices derived from n...
Cosmological covariance matrices are fundamental for parameter inference, since they are responsible...
International audienceWe compare the measurements of the bispectrum and the estimate of its covarian...
Accepted —. Received —;in original form — For galaxy clustering to provide robust constraints on cos...