Weak-lensing Peak Statistics in Mocks by the Inverse-Gaussianization Method

Abstract

Abstract Recent studies have shown the great power of peak counts in weak-lensing maps. In this work, we apply the inverse-Gaussianization method proposed in Yu et al. to produce weak-lensing convergence maps quickly and investigate the peak statistics, including the peak height counts and peak steepness counts in these mocks. The distributions of peak height and steepness are in good agreement with the simulation results. The difference is ≲20% for these peak statistics in the maps at source redshift z s = 1. Also, the loss of off-diagonal elements in the peak covariance motivates us to consider the super-sample variance in weak-lensing peak statistics. We propose four correction methods to effectively recover the (anti)correlation among different bins by adding different scatters in the mean value of these mocks. Finally, as an example of the application, we adopt the improved inverse-Gaussianization method to quickly generate 40,000 mocks to calculate precision matrices for the power spectrum and peak-statistics joint analysis.