Estimating the power spectrum covariance matrix with fewer mock samples

David W. Pearson*, Lado Samushia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Downloads (Pure)

Abstract

The covariance matrices of Power-Spectrum (P(k)) measurements from galaxy surveys are difficult to compute theoretically. The current best practice is to estimate covariance matrices by computing a sample covariance of a large number of mock catalogues. The next generation of galaxy surveys will require thousands of large volume mocks to determine the covariance matrices to desired accuracy. The errors in the inverse covariance matrix are larger and scale with the number of P(k) bins, making the problem even more acute. We develop a method of estimating covariance matrices using a theoretically justified, Few-Parameter model, calibrated with mock catalogues. Using a set of 600 BOSS DR11 mock catalogues, we show that a seven parameter model is sufficient to fit the covariance matrix of BOSS DR11 P(k) measurements. The covariance computed with this method is better than the sample covariance at any number of mocks and only ∼100 mocks are required for it to fully converge and the inverse covariance matrix converges at the same rate. This method shouldwork equally well for the next generation of galaxy surveys, although a demand for higher accuracy may require adding extra parameters to the fitting function.

Original languageEnglish
Pages (from-to)993-999
Number of pages7
JournalMonthly Notices of the Royal Astronomical Society
Volume457
Issue number1
Early online date27 Jan 2016
DOIs
Publication statusPublished - 21 Mar 2016

Keywords

  • Data Analysis-Galaxies
  • Methods
  • Statistics-Cosmological Parameters-Largescale structure of Universe

Fingerprint

Dive into the research topics of 'Estimating the power spectrum covariance matrix with fewer mock samples'. Together they form a unique fingerprint.

Cite this