标题： 关于在建模分辨的引力透镜图像中使用形状let 显示英文标题

标题： On the use of shapelets in modelling resolved, gravitationally lensed images

摘要： 分辨率图像数据的透镜建模在过去二十年间取得了快速发展。 最近，基于像素的方法逐渐流行起来，在这种方法中，源以非规则或自适应网格的形式重建。 通常情况下，源的重建是在贝叶斯框架下进行的，并由一组合理的先验指导。 我们讨论了基于形状分解法的方法集成到贝叶斯框架中的可能性，并量化了所需的正则化。 在这些方法中，源通过使用完整且正交基函数集的一个子集（称为形状分解）进行解析重建。 为了计算图像平面像素中的通量，将该像素分割为两个或更多三角形（取决于局部放大倍率），并对每个形状分解基函数在源平面上进行积分。 源的正则化（对源施加先验）也可以进行解析处理。 这种方法大大减少了源参数的数量，从数千减少到数百，并且得到的后验概率分布比基于像素的方法噪声更少。 显示英文摘要

摘要： Lens modeling of resolved image data has advanced rapidly over the past two decades. More recently pixel-based approaches, wherein the source is reconstructed on an irregular or adaptive grid, have become popular. Generally, the source reconstruction takes place in a Bayesian framework and is guided by a set of sensible priors. We discuss the integration of a shapelets-based method into a Bayesian framework and quantify the required regularization. In such approaches, the source is reconstructed analytically, using a subset of a complete and orthonormal set of basis functions, known as shapelets. To calculate the flux in an image plane pixel, the pixel is split into two or more triangles (depending on the local magnification), and each shapelet basis function is integrated over the source plane. Source regularization (enforcement of priors on the source) can also be performed analytically. This approach greatly reduces the number of source parameters from the thousands to hundreds and results in a posterior probability distribution that is much less noisy than pixel-based approaches.