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Taking into account the noise from intrinsic ellipticities of sourcegalaxies, we study the efficiency and completeness of halo detections from weaklensing convergence maps. Particularly, with numerical simulations, we comparethe Gaussian filter with the so called MRLens treatment based on themodification of the Maximum Entropy Method. For a pure noise field withoutlensing signals, a Gaussian smoothing results a residual noise field that isapproximately Gaussian in statistics if a large enough number of galaxies areincluded in the smoothing window. On the other hand, the noise field after theMRLens treatment is significantly non-Gaussian, resulting complications incharacterizing the noise effects. Considering weak-lensing cluster detections,although the MRLens treatment effectively deletes false peaks arising fromnoise, it removes the real peaks heavily due to its inability to distinguishreal signals with relatively low amplitudes from noise in its restorationprocess. The higher the noise level is, the larger the removal effects are forthe real peaks. For a survey with a source density n_g~30 arcmin^(2), thenumber 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 withM>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 inthe Gaussian smoothing case adds further advantages for this method tocircumvent the problem of the relatively low efficiency in weak-lensing clusterdetections. Therefore, in studies aiming to construct large cluster samplesfrom weak-lensing surveys, the Gaussian smoothing method performs significantlybetter than the MRLens.

Comparison of Halo Detection from Noisy Weak Lensing Convergence Maps with Gaussian Smoothing and MRLens Treatment