Analytical method for computing the covariance matrix of cosmic shear two-point correlation function

TL;DR

This paper evaluates analytical methods for estimating the covariance matrix of cosmic shear two-point correlation functions, emphasizing the importance of modeling off-diagonal elements accurately for reliable cosmological analyses.

Contribution

It compares several analytical covariance estimators, highlighting the limitations of the improved Narrow Kernel Approximation and demonstrating the effectiveness of the weighted quartic-counts method.

Findings

The iNKA method fails to reproduce off-diagonal covariance components accurately.

The weighted quartic-counts method shows better agreement with Gaussian simulations.

Accurate off-diagonal modeling is crucial for reliable weak lensing covariance estimates.

Abstract

Accurate estimation of the covariance matrix of cosmic shear statistics is essential for cosmological analyses using current and upcoming wide-area weak lensing surveys. In this work, we investigate analytical methods for computing the Gaussian covariance matrix of the cosmic shear two-point correlation function (2PCF), taking into account the effects of finite survey geometry. We compute the covariance of 2PCF based on the improved Narrow Kernel Approximation (iNKA), with a projection using the Legendre transformation. We also consider other analytical covariance estimators, the $f_{\mathrm{sky}}$ approximation and the weighted quartic-counts method. We evaluate the accuracy of those analytical methods using the convergence fields with the HSC Year 3 survey mask as a test case. We find that the covariance of the 2PCF obtained by using the iNKA does not reproduce the covariance measured directly from Gaussian simulations. Although the iNKA accurately models the diagonal structure of the harmonic-space covariance, residual inaccuracies in the off-diagonal components propagate through the Legendre transformation and significantly affect the real-space covariance. In contrast, the weighted quartic-counts method shows better agreement with the simulations. Our results demonstrate that accurate modeling of the off-diagonal structure of the harmonic-space covariance is crucial for obtaining reliable covariance estimates of real-space weak lensing statistics in the presence of survey window effects.