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

TL;DR

MADLens is a highly accurate, fast, and fully differentiable Python package for non-Gaussian lensing simulations, enabling advanced Bayesian inference and deep learning applications in cosmology.

Contribution

MADLens introduces a novel, efficient, and differentiable simulation tool for non-Gaussian lensing maps, combining high precision with low computational cost.

Findings

Power spectra agree with theoretical models up to L=10000

Achieves high accuracy with only 256^3 particles

Fully differentiable with respect to initial conditions and cosmological parameters

Abstract

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).