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PyAutoLens: Open-Source Strong Gravitational Lensing

Nightingale, James; Hayes, Richard; Kelly, Ashley; Amvrosiadis, Aristeidis; Etherington, Amy; He, Qiuhan; Li, Nan; Cao, XiaoYue; Frawley, Jonathan; Cole, Shaun; Enia, Andrea; Frenk, Carlos; Harvey, David; Li, Ran; Massey, Richard; Negrello, Mattia; Robertson, Andrew

PyAutoLens: Open-Source Strong Gravitational Lensing Thumbnail


Richard Hayes

Dr Qiuhan He
Post Doctoral Research Associate

Nan Li

XiaoYue Cao

Jonathan Frawley

Andrea Enia

David Harvey

Ran Li

Mattia Negrello


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.


Nightingale, J., Hayes, R., Kelly, A., Amvrosiadis, A., Etherington, A., He, Q., …Robertson, A. (2021). PyAutoLens: Open-Source Strong Gravitational Lensing. The Journal of Open Source Software, 6(58), Article 2825.

Journal Article Type Article
Online Publication Date Feb 20, 2021
Publication Date 2021-02
Deposit Date May 14, 2021
Publicly Available Date Jul 16, 2021
Journal The Journal of Open Source Software
Electronic ISSN 2475-9066
Publisher Open Journals
Peer Reviewed Peer Reviewed
Volume 6
Issue 58
Article Number 2825


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