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arXiv 2102.04127astro-ph.COastro-ph.IM
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利用稀疏性和高斯随机场重建弱透镜质量

Weak lensing mass reconstruction using sparsity and a Gaussian random field

J. -L. Starck, K. E. Themelis, N. Jeffrey, A. Peel, F. Lanusse

摘要:

我们介绍了一种新的方法来重建暗物质的质量图从弱引力透镜测量。所提出的方法的基石在于在宇宙中的物质密度场作为两个组件的混合物的一个新的建模:(1)一个基于稀疏性的组件,捕获非高斯结构的字段,如在不同的空间尺度的峰值或晕;和(2)高斯随机场,这是众所周知的,以及代表的线性特性的字段。我们提出了一种算法称为MCALens联合估计这两个组件。MCAlens是基于一个交替的最小化,结合稀疏恢复和一个近端迭代维纳滤波。模拟数据的实验结果表明,该方法具有更高的估计精度相比,国家的最先进的质量地图重建方法。

英文摘要:

We introduce a novel approach to reconstruct dark matter mass maps from weak gravitational lensing measurements. The cornerstone of the proposed method lies in a new modelling of the matter density field in the Universe as a mixture of two components:(1) a sparsity-based component that captures the non-Gaussian structure of the field, such as peaks or halos at different spatial scales; and (2) a Gaussian random field, which is known to well represent the linear characteristics of the field.Methods. We propose an algorithm called MCALens which jointly estimates these two components. MCAlens is based on an alternating minimization incorporating both sparse recovery and a proximal iterative Wiener filtering. Experimental results on simulated data show that the proposed method exhibits improved estimation accuracy compared to state-of-the-art mass map reconstruction methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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