Simulations of Wide-Field Weak Lensing Surveys II: Covariance Matrix of Real Space Correlation Functions - Astrophysics > Cosmology and Nongalactic AstrophysicsReport as inadecuate




Simulations of Wide-Field Weak Lensing Surveys II: Covariance Matrix of Real Space Correlation Functions - Astrophysics > Cosmology and Nongalactic Astrophysics - Download this document for free, or read online. Document in PDF available to download.

Abstract: Using 1000 ray-tracing simulations for a {\Lambda}-dominated cold dark modelin Sato et al. 2009, we study the covariance matrix of cosmic shearcorrelation functions, which is the standard statistics used in the previousmeasurements. The shear correlation function of a particular separation angleis affected by Fourier modes over a wide range of multipoles, even beyond asurvey area, which complicates the analysis of the covariance matrix. Toovercome such obstacles we first construct Gaussian shear simulations from the1000 realizations, and then use the Gaussian simulations to disentangle theGaussian covariance contribution to the covariance matrix we measured from theoriginal simulations. We found that an analytical formula of Gaussiancovariance overestimates the covariance amplitudes due to an effect of finitesurvey area. Furthermore, the clean separation of the Gaussian covarianceallows to examine the non-Gaussian covariance contributions as a function ofseparation angles and source redshifts. For upcoming surveys with typicalsource redshifts of z s=0.6 and 1.0, the non-Gaussian contribution to thediagonal covariance components at 1 arcminute scales is greater than theGaussian contribution by a factor of 20 and 10, respectively. Predictions basedon the halo model qualitatively well reproduce the simulation results, howevershow a sizable disagreement in the covariance amplitudes. By combining thesesimulation results we develop a fitting formula to the covariance matrix for asurvey with arbitrary area coverage, taking into account effects of thefiniteness of survey area on the Gaussian covariance.



Author: Masanori Sato, Masahiro Takada, Takashi Hamana, Takahiko Matsubara

Source: https://arxiv.org/







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