Improving weak lensing mass map reconstructions using Gaussian and Sparsity Priors: application to DES SV
Improving weak lensing mass map reconstructions using Gaussian and Sparsity Priors: application to DES SV
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DOI：
10.1093/mnras/sty1252
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发表时间：
2018-01
影响因子：
4.8
通讯作者：
N. Jeffrey;F. Abdalla;O. Lahav;F. Lanusse;J. Starck;A. Leonard;D. Kirk;C. Chang;E. Baxter
中科院分区：
文献类型：
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作者：
N. Jeffrey;F. Abdalla;O. Lahav;F. Lanusse;J. Starck;A. Leonard;D. Kirk;C. Chang;E. Baxter
使用弱引力透镜测量来绘制底层密度场（包括不可见暗物质）现在已成为宇宙学的标准工具。由于其对当前和即将进行的调查的科学结果的重要性，应该充分了解收敛重建方法的质量。我们比较了三种方法：Kaiser-Squires (KS)、维纳滤波器和 GLIMPSE。 Kaiser-Squires 是直接反演，不考虑调查掩模或噪声。维纳滤波器非常适合贝叶斯框架中的高斯密度场。 GLIMPSE 使用稀疏性，旨在重建密度场中的非线性。我们将这些方法与使用公共暗能量调查 (DES) 科学验证 (SV) 数据和实际 DES 模拟的多项测试进行比较。 Wiener 滤波器和 GLIMPSE 在一系列指标上比平滑的 Kaiser-Squires 提供了显着的改进。维纳滤波器和 GLIMPSE 收敛重建都显示，皮尔逊相关性与模拟的基本事实相比提高了 12%。为了比较映射方法找到质量峰值的能力，我们测量了模拟（n元逻辑和）CDM剪切目录和没有质量波动的目录（从峰值统计推断宇宙学时的标准数据向量）的峰值计数之间的差异；这些峰值统计数据的最大信噪比对于 Wiener 滤波器增加了 3.5 倍，对于 GLIMPSE 则增加了 9 倍。通过模拟，我们测量谐波相位的重建； GLIMPSE 使相位残差浓度提高了 17%，维纳滤波器使相位残差浓度提高了 18%。数据重建和前景 redMapPer 簇之间的相关性通过 Wiener 滤波器增加了 18%，通过 GLIMPSE 增加了 32%。
Mapping the underlying density field, including non-visible dark matter, using weak gravitational lensing measurements is now a standard tool in cosmology. Due to its importance to the science results of current and upcoming surveys, the quality of the convergence reconstruction methods should be well understood. We compare three methods: Kaiser-Squires (KS), Wiener filter, and GLIMPSE. Kaiser-Squires is a direct inversion, not accounting for survey masks or noise. The Wiener filter is well-motivated for Gaussian density fields in a Bayesian framework. GLIMPSE uses sparsity, aiming to reconstruct non-linearities in the density field. We compare these methods with several tests using public Dark Energy Survey (DES) Science Verification (SV) data and realistic DES simulations. The Wiener filter and GLIMPSE offer substantial improvements over smoothed Kaiser-Squires with a range of metrics. Both the Wiener filter and GLIMPSE convergence reconstructions show a 12 per cent improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods' abilities to find mass peaks, we measure the difference between peak counts from simulated (n-ary logical and) CDM shear catalogues and catalogues with no mass fluctuations (a standard data vector when inferring cosmology from peak statistics); the maximum signal-to-noise of these peak statistics is increased by a factor of 3.5 for the Wiener filter and 9 for GLIMPSE. With simulations, we measure the reconstruction of the harmonic phases; the phase residuals' concentration is improved 17 per cent by GLIMPSE and 18 per cent by the Wiener filter. The correlation between reconstructions from data and foreground redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per cent by GLIMPSE.