DICES combines binary-space-partition equal-area jackknives, correlation-matrix shrinkage, and delete-2 diagonal correction to yield non-singular, debiased covariances for Euclid clustering and weak-lensing spectra, cutting relative error 33% (covariance) and 48% (correlation) versus plain jackknife
Jackknife resampling technique on mocks: an alternative method for covariance matrix estimation
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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.
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Simulations of void-shear cross-correlation demonstrate that void lensing can constrain total neutrino mass to σ(M_ν)=0.096 eV without shape noise and 0.340 eV with Stage-III-like noise.
Estimators from squeezed bispectrum and collapsed trispectrum recover unbiased small-scale matter power spectrum covariance at the percent level using 25 Quijote simulations.
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If at First You Don't Succeed, Trispectrum: I. Estimating the Matter Power Spectrum Covariance with Higher-Order Statistics
Estimators from squeezed bispectrum and collapsed trispectrum recover unbiased small-scale matter power spectrum covariance at the percent level using 25 Quijote simulations.