Three-dimensional Reconstruction of Weak-lensing Mass Maps with a Sparsity Prior. I. Cluster Detection
Three-dimensional Reconstruction of Weak-lensing Mass Maps with a Sparsity Prior. I. Cluster Detection
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DOI：
10.3847/1538-4357/ac0625
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Xiangchong Li;N. Yoshida;M. Oguri;Shiro Ikeda;Wentao Luo
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作者：
Xiangchong Li;N. Yoshida;M. Oguri;Shiro Ikeda;Wentao Luo
我们提出了一种新的方法来重建高分辨率的三维质量地图使用光度弱透镜调查的数据。我们应用自适应LASSO算法进行基于稀疏性的重建的假设下，基本的宇宙密度场表示的总和Navarro-Frenk-白色晕。我们产生现实的模拟星系剪切目录考虑剪切变形从孤立的晕的配置匹配的斯巴鲁超Suprime-Cam调查与其光度红移估计。我们表明，自适应方法显着减少了视线拖尾，这是由在不同的红移的透镜内核之间的相关性。质量下限为1014.0 h−1 M、1014.7 h−1 M、1015.0 h−1 M的透镜星系团可以分别在低红移（z < 0.3）、中红移（0.3 ≤ z < 0.6）和高红移（0.6 ≤ z < 0.85）处以1.5σ的置信度被探测到，平均错误探测率为0.022 deg−2。对于z ≤ 0.4的晕，估计的星系团红移系统地低于真实值Δz ≤ 0.03，但对于0.4 < z ≤ 0.85的星系团，相对红移偏差小于0.5%。红移估计的标准偏差为0.092。我们的方法可以直接三维集群检测准确的红移估计。
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.