ThreeDimensional Reconstruction of Weak Lensing Mass Maps with a Sparsity Prior. I. Cluster Detection
Abstract
We propose a novel method to reconstruct highresolution threedimensional mass maps using data from photometric weaklensing surveys. We apply an adaptive LASSO algorithm to perform a sparsitybased reconstruction on the assumption that the underlying cosmic density field is represented by a sum of NavarroFrenkWhite halos. We generate realistic mock galaxy shape catalogues by considering the shear distortions from isolated halos for the configurations matched to Subaru Hyper SuprimeCam Survey with its photometric redshift estimates. We show that the adaptive method significantly reduces lineofsight smearing that is caused by the correlation between the lensing kernels at different redshifts. Lensing clusters with lower mass limits of $10^{14.0} h^{1}M_{\odot}$, $10^{14.7} h^{1}M_{\odot}$, $10^{15.0} h^{1}M_{\odot}$ can be detected with 1.5$\sigma$ confidence at the low ($z<0.3$), median ($0.3\leq z< 0.6$) and high ($0.6\leq z< 0.85$) 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 $\Delta z \sim 0.03$ for halos at $z\leq 0.4$, but the relative redshift bias is below $0.5\%$ for clusters at $0.4<z\leq 0.85$. The standard deviation of the redshift estimation is $0.092$. Our method enables direct threedimensional cluster detection with accurate redshift estimates.
 Publication:

arXiv eprints
 Pub Date:
 February 2021
 arXiv:
 arXiv:2102.09707
 Bibcode:
 2021arXiv210209707L
 Keywords:

 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 16 pages, 14 figures, submitted to ApJ