lenstronomy is a multi-purpose package to model strong gravitational lenses. The software package is presented in
Birrer & Amara 2018 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.
$ pip install lenstronomy --userSpecific instructions for settings and installation requirements for special cases that can provide speed-ups, we refer to the documentation page.
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.
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
Multiple affiliated packages that make use of lenstronomy can be found here (not complete) and further packages are under development by the community.
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!
Check out the contributing page and become an author of lenstronomy! A big shutout to the current list of contributors and developers!
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
The design concept of lenstronomy are reported in Birrer & Amara 2018.
Please cite this paper 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. Please make sure to cite also
the relevant work that was implemented in lenstronomy, as described in the release paper.