Three-dimensional Reconstruction of Weak-lensing Mass Maps with a Sparsity Prior. I. Cluster Detection

Abstract

Abstract We propose a novel method to reconstruct high-resolution three-dimensional mass maps using data from photometric weak-lensing surveys. We apply an adaptive LASSO algorithm to perform a sparsity-based reconstruction on the assumption that the underlying cosmic density field is represented by a sum of Navarro–Frenk–White halos. We generate realistic mock galaxy shear catalogs by considering the shear distortions from isolated halos for the configurations matched to the Subaru Hyper Suprime-Cam Survey with its photometric redshift estimates. We show that the adaptive method significantly reduces line-of-sight smearing that is caused by the correlation between the lensing kernels at different redshifts. Lensing clusters with lower mass limits of 1014.0 h−1 M ⊙, 1014.7 h−1 M ⊙, 1015.0 h−1 M ⊙ can be detected with 1.5σ confidence at the low (z < 0.3), median (0.3 ≤ z < 0.6), and high (0.6 ≤ z < 0.85) redshifts, respectively, with an average false detection rate of 0.022 deg−2. The estimated redshifts of the detected clusters are systematically lower than the true values by Δz ∼ 0.03 for halos at z ≤ 0.4, but the relative redshift bias is below 0.5% for clusters at 0.4 < z ≤ 0.85. The standard deviation of the redshift estimation is 0.092. Our method enables direct three-dimensional cluster detection with accurate redshift estimates.