Strong Gravitational Lens Inversion: A Bayesian Approach
Strong Gravitational Lens Inversion: A Bayesian Approach
复制标题
DOI：
10.1086/498409
复制
发表时间：
2005-09
期刊：
影响因子：
--
通讯作者：
B. Brewer;G. Lewis
中科院分区：
文献类型：
--
作者：
B. Brewer;G. Lewis
如果一个扩展的源，比如一个星系，被前景中的一个大质量物体引力透镜化，透镜化会扭曲观察到的图像。对于特定的透镜和光源组合，模拟观察到的图像是简单的。在实践中，人们观察天空中的透镜图像，尽管由于大气和望远镜效应而变得模糊，并且还受到噪声的污染。接下来的问题是，如果数据不完整，那么透镜质量分布和光源表面亮度分布的什么样的组合才能合理地产生这幅图像？这是一个典型的反问题的例子，解决它的方法是由贝叶斯推理的框架。在本文中，我们演示了应用贝叶斯推理的重力透镜重建的问题，并说明使用马尔可夫链蒙特卡罗模拟，可以使用时，分析计算变得太困难。以前的方法进行重力透镜反演被视为在一个新的光，作为特殊情况下，本文提出的一般方法。因此，我们能够回答，至少在原则上，挥之不去的问题，在重建源和透镜参数的不确定性，考虑到所有的数据和任何先验信息，我们可能有。
If an extended source, such as a galaxy, is gravitationally lensed by a massive object in the foreground, the lensing distorts the observed image. It is straightforward to simulate what the observed image would be for a particular lens and source combination. In practice, one observes the lensed image on the sky, albeit blurred by atmospheric and telescopic effects and also contaminated with noise. The question that then arises is, given this incomplete data, what combinations of lens mass distribution and source surface brightness profile could plausibly have produced this image? This is a classic example of an inverse problem, and the method for solving it is given by the framework of Bayesian inference. In this paper we demonstrate the application of Bayesian inference to the problem of gravitational lens reconstruction and illustrate the use of Markov Chain Monte Carlo simulations, which can be used when the analytical calculations become too difficult. Previous methods for performing gravitational lens inversion are seen in a new light, as special cases of the general approach presented in this paper. Thus, we are able to answer, at least in principle, lingering questions about the uncertainties in the reconstructed source and lens parameters, taking into account all of the data and any prior information we may have.