MADLens, a 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 2563 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.

Recommended citation: V. Boehm, Y. Feng, M. E. Lee et al. (2021) "MADLens, a Package for Fast and Differentiable Non-Gaussian Lensing Simulations." Astronomy & Computing.
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