autogalaxy 2026.4.5.3

Open-Source Multi Wavelength Galaxy Structure & Morphology

Installation Guide | readthedocs | Introduction on Colab HowToGalaxy

PyAutoGalaxy is software for analysing the morphologies and structures of galaxies:

PyAutoGalaxy also fits interferometer data from observatories such as ALMA:

Getting Started

The following links are useful for new starters:

• The PyAutoGalaxy readthedocs, which includes an overview of PyAutoGalaxy’s core features, a new user starting guide and an installation guide.

• The introduction Jupyter Notebook on Google Colab, where you can try PyAutoGalaxy in a web browser (without installation).

• The autogalaxy_workspace GitHub repository, which includes example scripts and the HowToGalaxy Jupyter notebook lectures which give new users a step-by-step introduction to PyAutoGalaxy.

Core Aims

PyAutoGalaxy has three core aims:

• Big Data: Scaling automated Sérsic fitting to extremely large datasets, accelerated with JAX on GPUs and using tools like an SQL database to **build a scalable scientific workflow**.

• Model Complexity: Fitting complex galaxy morphology models (e.g. Multi Gaussian Expansion, Shapelets, Ellipse Fitting, Irregular Meshes) that go beyond just simple Sérsic fitting.

• Data Variety: Support for many data types (e.g. CCD imaging, interferometry, multi-band imaging) which can be fitted independently or simultaneously.

A complete overview of the software’s aims is provided in our Journal of Open Source Software paper.

Community & Support

Support for PyAutoGalaxy is available via our Slack workspace, where the community shares updates, discusses galaxy modeling and analysis, and helps troubleshoot problems.

Slack is invitation-only. If you’d like to join, please send an email requesting an invite.

For installation issues, bug reports, or feature requests, please raise an issue on the GitHub issues page.

Here’s a clean, AutoGalaxy-appropriate rewrite, keeping the same structure and tone but removing lensing-specific language:

HowToGalaxy

For users less familiar with galaxy analysis, Bayesian inference, and scientific analysis, you may wish to read through the HowToGalaxy lectures. These introduce the basic principles of galaxy modeling and Bayesian inference, with the material pitched at undergraduate level and above.

A complete overview of the lectures is provided on the HowToGalaxy readthedocs page

Citations

Information on how to cite PyAutoGalaxy in publications can be found on the citations page.

Contributing

Information on how to contribute to PyAutoGalaxy can be found on the contributing page.

Hands on support for contributions is available via our Slack workspace, again please email to request an invite.