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

Böhm, V.; Feng, Y.; Lee, M. E.; Dai, B.

doi:10.1016/j.ascom.2021.100490

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

Journal Article · Tue Aug 10 00:00:00 EDT 2021 · Astronomy and Computing

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

^[1]; Feng, Y. ^[2];

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

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1864171

Alternate ID(s):: OSTI ID: 1862516

Journal Information:: Astronomy and Computing, Vol. 36; ISSN 2213-1337

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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

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