Jackknife resampling technique on mocks: an alternative method for covariance matrix estimation

Abstract

We 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.

arXiv comment11 pages, 11 figures, 2 tables

References in corpus (2)

• Euclid Definition Study Report
• The DESI Experiment, a whitepaper for Snowmass 2013

Cited by in corpus (10)

• The integrated 3-point correlation function of cosmic shear
• Non-linear shrinkage estimation of large-scale structure covariance
• Super-sample covariance approximations and partial sky coverage
• Galaxy 2-Point Covariance Matrix Estimation for Next Generation Surveys
• Extracting cosmological information from the angular power spectrum of the 2MASS Photometric Redshift catalogue
• Large Covariance Matrices: Accurate Models Without Mocks
• CARPool Covariance: Fast, unbiased covariance estimation for large-scale structure observables
• Exploring the cosmological synergy between galaxy cluster and cosmic void number counts
• Galaxy and Mass Assembly (GAMA): Probing galaxy-group correlations in redshift space with the halo streaming model
• Residual Smoothing: Using Mocks to Correct Model Covariance Matrices