lenstronomy - gravitational lensing software package

lenstronomy is a multi-purpose package to model strong gravitational lenses. The software package is presented in Birrer & Amara 2018 and Birrer et al. 2021 , and is based on Birrer et al 2015. lenstronomy finds application for time-delay cosmography and measuring the expansion rate of the Universe, for quantifying lensing substructure to infer dark matter properties, morphological quantification of galaxies, quasar-host galaxy decomposition and much more. A (incomplete) list of publications making use of lenstronomy can be found at this link.

The development is coordinated on GitHub and contributions are welcome. The documentation of lenstronomy is available at readthedocs.org and the package is distributed over PyPI. lenstronomy is an affiliated package of astropy.

Installation

$ pip install lenstronomy --user

Specific instructions for settings and installation requirements for special cases that can provide speed-ups, we refer to the documentation page.

Getting started

The starting guide jupyter notebook leads through the main modules and design features of lenstronomy. The modular design of lenstronomy allows the user to directly access a lot of tools and each module can also be used as stand-alone packages.

If you are new to gravitational lensing, check out the mini lecture series giving an introduction to gravitational lensing with interactive Jupyter notebooks in the cloud.

Example notebooks

We have made an extension module available at https://github.com/sibirrer/lenstronomy_extensions. You can find simple examle notebooks for various cases. The latest versions of the notebooks should be compatible with the recent pip version of lenstronomy.

• Units, coordinate system and parameter definitions in lenstronomy
• FITS handling and extracting needed information from the data prior to modeling
• Modeling a simple Einstein ring
• Quadrupoly lensed quasar modelling
• Double lensed quasar modelling
• Time-delay cosmography
• Source reconstruction and deconvolution with Shapelets
• Solving the lens equation
• Multi-band fitting
• Measuring cosmic shear with Einstein rings
• Fitting of galaxy light profiles, like e.g. GALFIT
• Quasar-host galaxy decomposition
• Hiding and seeking a single subclump
• Mock generation of realistic images with substructure in the lens
• Mock simulation API with multi color models
• Catalogue data modeling of image positions, flux ratios and time delays
• Example of numerical ray-tracing and convolution options
• Simulated lenses with populations generated by SkyPy

Affiliated packages

Multiple affiliated packages that make use of lenstronomy can be found here (not complete) and further packages are under development by the community.

Mailing list and Slack channel

You can join the lenstronomy mailing list by signing up on the google groups page.

The email list is meant to provide a communication platform between users and developers. You can ask questions, and suggest new features. New releases will be announced via this mailing list.

We also have a Slack channel for the community. Please send me an email such that I can add you to the channel.

If you encounter errors or problems with lenstronomy, please let us know!

Contribution

Check out the contributing page and become an author of lenstronomy! A big shutout to the current list of contributors and developers!

Shapelet reconstruction demonstration movies

We provide some examples where a real galaxy has been lensed and then been reconstructed by a shapelet basis set.

• HST quality data with perfect knowledge of the lens model
• HST quality with a clump hidden in the data
• Extremely large telescope quality data with a clump hidden in the data

Attribution

The design concept of lenstronomy is reported by Birrer & Amara 2018. The current JOSS software publication is presented by Birrer et al. 2021. Please cite these two publications when you use lenstronomy in a publication and link to https://github.com/sibirrer/lenstronomy. Please also cite Birrer et al 2015 when you make use of the lenstronomy work-flow or the Shapelet source reconstruction and make sure to cite also the relevant work that was implemented in lenstronomy, as described in the release paper and the documentation. Don't hesitate to reach out to the developers if you have questions!