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