Performance of internal covariance estimators for cosmic shear correlation functions

O. Friedrich, S. Seitz, T. F. Eifler, D. Gruen

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Data re-sampling methods such as delete-one jackknife, bootstrap or the sub-sample covariance are common tools for estimating the covariance of large-scale structure probes. We investigate different implementations of these methods in the context of cosmic shear two-point statistics. Using lognormal simulations of the convergence field and the corresponding shear field we generate mock catalogues of a known and realistic covariance. For a survey of ~5000 deg2 we find that jackknife, if implemented by deleting sub-volumes of galaxies, provides the most reliable covariance estimates. Bootstrap, in the common implementation of drawing sub-volumes of galaxies, strongly overestimates the statistical uncertainties. In a forecast for the complete 5-yr Dark Energy Survey, we show that internally estimated covariance matrices can provide a large fraction of the true uncertainties on cosmological parameters in a 2D cosmic shear analysis. The volume inside contours of constant likelihood in the Ωm8 plane as measured with internally estimated covariance matrices is on average ≳85 per cent of the volume derived from the true covariance matrix. The uncertainty on the parameter combination Σ8 ~ σ8Ω0.5m derived from internally estimated covariances is ~90 per cent of the true uncertainty.

Original languageEnglish (US)
Pages (from-to)2662-2680
Number of pages19
JournalMonthly Notices of the Royal Astronomical Society
Issue number3
StatePublished - Mar 1 2016


  • Cosmological parameters
  • Data analysis
  • Largescale structure of Universe
  • Methods: statistical

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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