Gaussianizing the non-Gaussian lensing convergence field II: the applicability to noisy data

In paper I (Yu et al. [1]), we show through N-body simulation that a local monotonic Gaussian transformation can significantly reduce non-Gaussianity in a noise-free lensing convergence field. This makes the Gaussianization a promising theoretical tool to understand high-order lensing statistics. Here we present a study of its applicability in lensing data analysis, in particular when shape measurement noise is presented in lensing convergence maps. (i) We find that shape measurement noise significantly degrades the Gaussianization performance and the degradation increases for shallower surveys. (ii) The Wiener filter is efficient in reducing the impact of shape measurement noise. The Gaussianization of the Wiener-filtered lensing maps is able to suppress skewness, kurtosis, and the 5th- and 6th-order cumulants by a factor of 10 or more. It also works efficiently to reduce the bispectrum to zero.