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

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).

10 pages, 15 figures. Update matches version submitted to journal. Acknowledgments added and typos fixed

References in corpus (18)

• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• KiDS-450: Cosmological parameter constraints from tomographic weak gravitational lensing
• LSST Science Book, Version 2.0
• Weak Lensing and Dark Energy
• FastPM: a new scheme for fast simulations of dark matter and halos
• CFHTLenS: Cosmological constraints from a combination of cosmic shear two-point and three-point correlations
• Weak Lensing Probes of Modified Gravity
• Three-Point Correlations in Weak Lensing Surveys: Model Predictions and Applications
• Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data
• Dark Energy and Matter Evolution from Lensing Tomography
• 3D Cosmic Shear: Cosmology from CFHTLenS
• Cosmological Constraints From Weak Lensing Peak Statistics With CFHT Stripe 82 Survey
• Hierarchical Cosmic Shear Power Spectrum Inference
• Determination of Cosmological Parameters
• Cosmological constraints from the capture of non-Gaussianity in Weak Lensing data
• Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
• Bayesian forward modelling of cosmic shear data
• Weak lensing peak count as a probe of f(R) theories