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 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 iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the 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 IllustrisTNG cosmological hydrodynamic simulation.

翻译：对强引力透镜系统进行建模以量化背景源图像的畸变并重构前景透镜中的质量密度，一直是一项艰巨的计算挑战。随着引力透镜图像质量的提升，充分利用其所含信息的任务在计算和算法层面变得愈加困难。在本研究中，我们采用基于递推推理机（RIM）的神经网络，同时将背景源的无畸变图像和透镜质量密度分布重构为像素化地图。该方法通过利用物理模型（光线追踪模拟）来学习基于数据优化似然性的过程，并借助神经网络通过训练数据隐式学习的先验进行正则化，从而迭代重构模型参数（源的图像和像素化密度图）。相较于传统参数化模型，所提方法具有显著更强的表达能力，能够重构复杂质量分布——我们通过使用来自IllustrisTNG宇宙学流体动力学模拟的逼真透镜星系对此进行了验证。