paltas 0.1.0

Strong lens substructure package.

paltas is a package for conducting simulation-based inference on strong gravitational lensing images. The package builds on lenstronomy to create large datasets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST’s COSMOS field. paltas also includes the capability to easily train neural posterior estimators of the parameters of the lensing system and to run hierarchical inference on test populations.

Installation

paltas is installable via pip:

$ pip install paltas

The default paltas requirements do not include tensorflow, but if you are interested in using the modules contained in the Analysis folder, you will have to install tensorflow:

$ pip install tensorflow

Usage

The main functionality of paltas is to generate realistic datasets of strong gravitational lenses in a way that’s modular, scalable, and user-friendly. To make a dataset with platas all you need is a configuration file which you can then pass to the generate.py script:

$ python generate.py path/to/config/file path/to/output/folder --n 100

Running the line of code above would generate 100 lenses and output them in the specified folder. paltas comes preloaded with a number of configuration files which are described in Configs/Examples/README.rst. For example, to create a dataset with HST observational effects, subhalos, and line-of-sight halos run:

$ python generate.py Configs/Examples/config_all.py example --n 100

We provide a tutorial notebook that describes how to generate your own config file.

Demos

paltas comes with a tutorial notebook for users interested in modifying the simulation classes.

• Implement your own source, line-of-sight, subhalo, or main deflector model.

• Training a neural posterior estimator of simulation parameters.

• Running hierarchical inference on a population of strong lenses.

Figures

Code for generating the plots included in some of the publications using paltas can be found under the corresponding arxiv number in the notebooks/papers/ folder.

Attribution

If you use paltas or its datasets for your own research, please cite the paltas package (Wagner-Carena et al. 2022) as well as the lenstronomy package (Birrer & Amara 2018, Birrer et al. 2021).