论文标题
Madlens，用于快速和可区分非高斯镜头模拟的Python包装
MADLens, a python package for fast and differentiable non-Gaussian lensing simulations
论文作者
论文摘要
我们为Madlens提供了一个Python软件包，用于在任意源红移中以前所未有的精度生产非高斯镜头收敛图。 Madlens旨在达到高精度，同时保持计算成本尽可能低。一个只有$ 256^3 $颗粒的Madlens模拟会产生收敛图，其功率与Halofit的精确限制范围内的理论镜头功率光谱一致。通过高度可行的粒子网算法，镜头投影中的子进化方案以及机器学习启发的锐化步骤的组合，这是可能的。此外，对于基础粒子模拟的初始条件和许多宇宙学参数，Madlens是完全可区分的。这些属性允许Madlens用作需要优化或衍生化辅助采样的贝叶斯推理算法中的正向模型。 Madlens的另一个用例是生产大型高分辨率模拟集，因为它们是培训新型基于深度学习的镜头分析工具所需的。我们将Madlens包在Creative Commons许可证（https://github.com/vmboehm/madlens）中公开提供。
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 agree 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 (https://github.com/VMBoehm/MADLens).
