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

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

2010-12-29

arxiv.org/abs/1012.5894v1

Astronomy and Astrophysics

Astrophysics

Instrumentation and Methods for Astrophysics

16 pages, 8 figures, accepted for publication by RAA

Scientific paper

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.

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