The Astrophysical Journal, vol. 916, no. 2, pp. 67(13pp)

著者:

  • Xiangchong Li
  • Naoki Yoshida
  • Masamune Oguri
  • Shiro Ikeda
  • Wentao Luo

キーワード:

  • weak-lensing mass maps
  • sparse modeling

URL:

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 $10^14.0$ {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 ($0.3łe z < 0.6$), and high ($0.6 łe z < 0.85$) 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 $z łe 0.4$, but the relative redshift bias is below 0.5 % for clusters at $0.4 < z łe 0.85$. The standard deviation of the redshift estimation is $0.092$. Our method enables direct three-dimensional cluster detection with accurate redshift estimates.