The point spread function reconstruction by using Moffatlets — I
The point spread function reconstruction by using Moffatlets — I
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DOI：
10.1088/1674-4527/16/9/139
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发表时间：
2016-04
影响因子：
1.8
通讯作者：
Baishun Li;Guoliang Li;Jun Cheng;J. Peterson;Wei Cui
中科院分区：
文献类型：
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作者：
Baishun Li;Guoliang Li;Jun Cheng;J. Peterson;Wei Cui
剪切测量是当前和未来弱透镜测量项目中的一项重要任务。点扩散函数（PSF）的重建是这个过程中涉及的重要步骤之一。在这项工作中，我们提出了三种不同的方法，Gaussianlets，Moffatlets和期望最大化主成分分析（EMPCA），并量化他们的效率PSF重建使用四套模拟的大型综合巡天望远镜（LSST）的星星图像。Gaussianlets和Moffatlets是两组不同的基函数，它们的轮廓分别基于Gaussian和Moffat函数。EMPCA是一种统计方法，它通过迭代的过程来寻找星星图像的主成分。我们的测试表明：（1）Moffatlets总是比Gaussianlets性能更好。(2)EMPCA算法具有结构紧凑、灵活性强等优点，但存在于PC中的噪声会影响PSF的大小和椭圆度。相比之下，Moffatlet保持的大小和椭圆度非常好。
Shear measurement is a crucial task in current and future weak lensing survey projects. The reconstruction of the point spread function (PSF) is one of the essential steps involved in this process. In this work, we present three different methods, Gaussianlets, Moffatlets and Expectation Maximization Principal Component Analysis (EMPCA), and quantify their efficiency on PSF reconstruction using four sets of simulated Large Synoptic Survey Telescope (LSST) star images. Gaussianlets and Moffatlets are two different sets of basis functions whose profiles are based on Gaussian and Moffat functions respectively. EMPCA is a statistical method performing an iterative procedure to find the principal components (PCs) of an ensemble of star images. Our tests show that: (1) Moffatlets always perform better than Gaussianlets. (2) EMPCA is more compact and flexible, but the noise existing in the PCs will contaminate the size and ellipticity of PSF. By contrast, Moffatlets keep the size and ellipticity very well.