论文标题
使用经常发作的推理机对引力镜片的像素化重建
Pixelated Reconstruction of Gravitational Lenses using Recurrent Inference Machines
论文作者
论文摘要
建模强力透镜，以量化背景源图像中的扭曲并重建前景镜头中的质量密度，传统上是一个困难的计算挑战。随着引力镜头图像的质量的提高，完全利用它们所包含的信息的任务在计算和算法上变得更加困难。在这项工作中，我们使用基于经常推理机（RIM）的神经网络同时重建背景源的未变形图像和镜头质量密度分布作为像素地图。我们迭代地提出的方法通过学习优化其可能性的过程（通过物理模型（一个射线追踪模拟）），通过神经网络通过其训练数据被神经网络隐含地学到的先验正规化的数据来重建模型参数（源和密度图像素）。与更传统的参数模型相比，所提出的方法具有明显的表达性，并且可以重建复杂的质量分布，我们通过使用从宇宙流体动力学模拟Illustristng采集的逼真的镜头星系来证明这一点。
Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass density in the foreground lenses has traditionally been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the Recurrent Inference Machine (RIM) to simultaneously reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method we present iteratively reconstructs the model parameters (the source and density map pixels) by learning the process of optimization of their likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the cosmological hydrodynamic simulation IllustrisTNG.
