MADLens, a python package for fast and differentiable non-Gaussian lensing simulations

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

MADLens is a Python package designed to produce non-Gaussian lensing convergence maps with unprecedented precision at arbitrary source redshifts. It achieves high accuracy through a combination of highly parallelizable algorithms, a sub-evolution scheme in the lensing projection, and a machine-learning inspired sharpening step. MADLens is fully differentiable with respect to initial conditions and cosmological parameters, making it useful for Bayesian inference algorithms and large, high-resolution simulation sets for deep-learning-based lensing analysis tools.