Creating jackknife and bootstrap estimates of the covariance matrix for the two-point correlation function

ABSTRACT We present correction terms that allow delete-one Jackknife and Bootstrap methods to be used recover unbiased estimates of the data covariance matrix two-point correlation function $\xi \left(\mathbf {r}\right)$. demonstrate accuracy precision this new method using a large set 1000 QUIJOTE simulations each cover comoving volume $1\rm {\left[h^{-1}Gpc\right]^3}$. The corrected resampling techniques correct amplitude structure as represented by its principal components within ∼10 per cent, level error achievable with size sample for test. Our corrections internal are shown robust against intrinsic clustering cosmological tracers both in real- redshift space two snapshots at z = 0 1 mimic samples significantly different clustering. also analyse slicing simulation into $\, n_{\rm sv}\, =64$ or 125 sub-samples show main impact sv}\,$ is on due limited number independent realizations can made given fixed sv}\,$.