Yu Yu, Pengjie Zhang, Weipeng Lin, Weiguang Cui, James N. Fry
In paper I (Yu et al. 2011 [1]), we show through N-body simulation that a
local monotonic Gaussian transformation can significantly reduce
non-Gaussianity in 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. (1) We find that shape measure- ment noise significantly
degrades the Gaussianization performance and the degradation increases for
shallower surveys. (2) Wiener filter is efficient to reduce the impact of shape
measurement noise. The Gaussianization of the Wiener filtered lensing maps is
able to suppress skewness, kurtosis, 5th- and 6th-order cumulants by a factor
of 10 or more. It also works efficiently to reduce the bispectrum well to zero.
View original:
http://arxiv.org/abs/1201.4527
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