Fast generation of weak lensing maps by the inverse-Gaussianization method

TL;DR

The paper introduces a fast inverse-Gaussianization method for generating numerous realistic weak lensing maps, enabling efficient noise analysis for upcoming surveys.

Contribution

It presents a novel, efficient technique to produce statistically independent weak lensing maps using local transformations, significantly reducing computational costs.

Findings

Generated maps match the power spectra and bispectra of simulations.

The method accurately reproduces the covariance matrix of the power spectrum.

Produced 16,384 maps to analyze the lensing power spectrum distribution.

Abstract

To take full advantage of the unprecedented power of upcoming weak lensing surveys, understanding the noise, such as cosmic variance and geometry/mask effects, is as important as understanding the signal itself. Accurately quantifying the noise requires a large number of statistically independent mocks for a variety of cosmologies. This is impractical for weak lensing simulations, which are costly for simultaneous requirements of large box size (to cover a significant fraction of the past light cone) and high resolution (to robustly probe the small scale where most lensing signal resides). Therefore fast mock generation methods are desired and are under intensive investigation. We propose a new fast weak lensing map generation method, named the inverse-Gaussianization method, based on the finding that a lensing convergence field can be Gaussianized to excellent accuracy by a local transformation [Yu et al, Phys. Rev. D 84, 023523 (2011)]. Given a simulation, it enables us to produce as many as infinite statistically independent lensing maps as fast as producing the simulation initial conditions. The proposed method is tested against simulations for each tomography bin centered at lens redshift $z \sim 0.5$, 1, and 2, with various statistics. We find that the lensing maps generated by our method have reasonably accurate power spectra, bispectra, and power spectrum covariance matrix. Therefore, it will be useful for weak lensing surveys to generate realistic mocks. As an example of application, we measure the probability distribution function of the lensing power spectrum, from 16384 lensing maps produced by the inverse-Gaussianization method.