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PyAutoLens: Open-Source Strong Gravitational Lensing

Nightingale, James. and Hayes, Richard and Kelly, Ashley and Amvrosiadis, Aristeidis and Etherington, Amy and He, Qiuhan and Li, Nan and Cao, XiaoYue and Frawley, Jonathan and Cole, Shaun and Enia, Andrea and Frenk, Carlos and Harvey, David and Li, Ran and Massey, Richard and Negrello, Mattia and Robertson, Andrew (2021) 'PyAutoLens: Open-Source Strong Gravitational Lensing.', The Journal of Open Source Software, 6 (58). p. 2825.

Abstract

Strong gravitational lensing, which can make a background source galaxy appears multiple times due to its light rays being deflected by the mass of one or more foreground lens galaxies, provides astronomers with a powerful tool to study dark matter, cosmology and the most distant Universe. PyAutoLens is an open-source Python 3.6+ package for strong gravitational lensing, with core features including fully automated strong lens modeling of galaxies and galaxy clusters, support for direct imaging and interferometer datasets and comprehensive tools for simulating samples of strong lenses. The API allows users to perform ray-tracing by using analytic light and mass profiles to build strong lens systems. Accompanying PyAutoLens is the autolens workspace, which includes example scripts, lens datasets and the HowToLens lectures in Jupyter notebook format which introduce non-experts to strong lensing using PyAutoLens. Readers can try PyAutoLens right now by going to the introduction Jupyter notebook on Binder or checkout the readthedocs for a complete overview of PyAutoLens’s features.

Item Type:Article
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution 4.0.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.21105/joss.02825
Publisher statement:Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Date accepted:No date available
Date deposited:16 July 2021
Date of first online publication:20 February 2021
Date first made open access:16 July 2021

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