Comparison of Halo Detection from Noisy Weak Lensing Convergence Maps
  with Gaussian Smoothing and MRLens Treatment
  Taking into account the noise from intrinsic ellipticities of source
galaxies, we study the efficiency and completeness of halo detections from weak
lensing convergence maps. Particularly, with numerical simulations, we compare
the Gaussian filter with the so called MRLens treatment based on the
modification of the Maximum Entropy Method. For a pure noise field without
lensing signals, a Gaussian smoothing results a residual noise field that is
approximately Gaussian in statistics if a large enough number of galaxies are
included in the smoothing window. On the other hand, the noise field after the
MRLens treatment is significantly non-Gaussian, resulting complications in
characterizing the noise effects. Considering weak-lensing cluster detections,
although the MRLens treatment effectively deletes false peaks arising from
noise, it removes the real peaks heavily due to its inability to distinguish
real signals with relatively low amplitudes from noise in its restoration
process. The higher the noise level is, the larger the removal effects are for
the real peaks. For a survey with a source density n_g~30 arcmin^(2), the
number of peaks found in an area of 3x3 sq.deg after MRLens filtering is only
~50 for the detection threshold kappa=0.02, while the number of halos with
M>5x10^{13} M_{\odot} and with redshift z<=2 in the same area is expected to be
~530. For the Gaussian smoothing treatment, the number of detections is ~260,
much larger than that of the MRLens. The Gaussianity of the noise statistics in
the Gaussian smoothing case adds further advantages for this method to
circumvent the problem of the relatively low efficiency in weak-lensing cluster
detections. Therefore, in studies aiming to construct large cluster samples
from weak-lensing surveys, the Gaussian smoothing method performs significantly
better than the MRLens.
