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

Journal Article · 09 August 2021 · Astronomy and Computing

DOI: https://doi.org/10.1016/j.ascom.2021.100490 · OSTI ID:1864171

Böhm, V.  ^[1]; Feng, Y. ^[2]; Lee, M. E.  ^[2]; Dai, B.  ^[2]

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1. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2. Univ. of California, Berkeley, CA (United States)

Here, 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 particles produces convergence maps whose power agrees with theoretical lensing power spectra up to 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

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1864171
• Journal Information: Astronomy and Computing, Journal Name: Astronomy and Computing Vol. 36; ISSN 2213-1337
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English