We present MADLens a python package for producing non-Gaussian lensing convergence maps at arbitrary source redshifts with unprecedented precision. MADLens is designed to achieve high accuracy while keeping computational costs as low as possible. A MADLens simulation with only $256^3$ particles produces convergence maps whose power agrees with theoretical lensing power spectra up to $L=10000$ within the accuracy limits of HaloFit. This is made possible by a combination of a highly parallelizable particle-mesh algorithm, a sub-evolution scheme in the lensing projection, and a machine-learning inspired sharpening step. Further, MADLens is fully differentiable with respect to the initial conditions of the underlying particle-mesh simulations and a number of cosmological parameters. These properties allow MADLens to be used as a forward model in Bayesian inference algorithms that require optimization or derivative-aided sampling. Another use case for MADLens is the production of large, high resolution simulation sets as they are required for training novel deep-learning-based lensing analysis tools. We make the MADLens package publicly available under a Creative Commons License

我们提出了 MADLens 一个 python 包，用于以前所未有的精度在任意源红移处生成非高斯透镜收敛图。MADLens 旨在实现高精度，同时尽可能降低计算成本。一个 MADLens 模拟只有$256^3$ 粒子产生收敛图，其功率与理论透镜功率谱一致 $升=10000$在 HaloFit 的准确度范围内。这是通过高度并行化的粒子网格算法、透镜投影中的子进化方案和机器学习启发的锐化步骤的组合实现的。此外，相对于基础粒子网格模拟的初始条件和许多宇宙学参数，MADLens 是完全可微的。这些属性允许 MADLens 在需要优化或导数辅助采样的贝叶斯推理算法中用作前向模型。MADLens 的另一个用例是生成大型、高分辨率的模拟集，因为它们是训练基于深度学习的新型透镜分析工具所必需的。我们根据知识共享许可公开提供 MADLens 包