docs: convert remaining prose .rst to MyST .md (pass 2)

CITATIONS.md

@@ -0,0 +1,26 @@
+# Citations & References
+
+The bibtex entries for **PyAutoLens** and its affiliated software packages can be found
+[here](https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.bib), with example text for citing **PyAutoLens**
+in [.tex format here](https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.tex) format here and
+[.md format here](https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.md). As shown in the examples, we
+would greatly appreciate it if you mention **PyAutoLens** by name and include a link to our GitHub page!
+
+**PyAutoLens** is published in the [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.02825#) and its
+entry in the above .bib file is under the citation key `pyautolens`. Please also cite the MNRAS AutoLens
+papers (<https://academic.oup.com/mnras/article/452/3/2940/1749640> and <https://academic.oup.com/mnras/article-abstract/478/4/4738/5001434?redirectedFrom=fulltext>) which are included
+under the citation keys `Nightingale2015` and `Nightingale2018`.
+
+You should also specify the non-linear search(es) you use in your analysis (e.g. Dynesty, Emcee, PySwarms, etc) in
+the main body of text, and delete as appropriate any packages your analysis did not use. The citations.bib file includes
+the citation key for all of these projects.
+
+If you use decomposed mass models (e.g. stellar mass models like an `Sersic` or dark matter models like
+an `NFW`) please cite the following paper <https://arxiv.org/abs/2106.11464> under
+citation key `Oguri2021`. Our deflection angle calculations are based on this method.
+
+If you specifically use a decomposed mass model with the `gNFW` please cite the following paper <https://academic.oup.com/mnras/article/488/1/1387/5526256> under
+citation key `Anowar2019`.
+
+The citations.bib file above also includes my work on [using strong lensing to study galaxy structure](https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.2049N/abstract). If you're feeling kind, please go ahead and stick
+a citation in your introduction using citep\{Nightingale2019} or [@Nightingale2019] ;).

MANIFEST.in

@@ -1,8 +1,8 @@
 # MANIFEST.in
 exclude .gitignore
-include README.rst
+include README.md
 include setup.cfg
-include CITATIONS.rst
+include CITATIONS.md
 include LICENSE
 include requirements.txt
 include optional_requirements.txt

README.md

@@ -0,0 +1,60 @@
+# PyAutoLens-JAX: Open-Source Strong Lensing
+
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.4/start_here.ipynb)
+[![Documentation Status](https://readthedocs.org/projects/pyautolens/badge/?version=latest)](https://pyautolens.readthedocs.io/en/latest/?badge=latest)
+[![Tests](https://github.com/Jammy2211/PyAutoLens/actions/workflows/main.yml/badge.svg)](https://github.com/Jammy2211/PyAutoLens/actions)
+[![Build](https://github.com/Jammy2211/PyAutoBuild/actions/workflows/release.yml/badge.svg)](https://github.com/Jammy2211/PyAutoBuild/actions)
+[![Code Style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![JOSS](https://joss.theoj.org/papers/10.21105/joss.02825/status.svg)](https://doi.org/10.21105/joss.02825)
+[![Zenodo DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4548697.svg)](https://doi.org/10.5281/zenodo.4548697)
+[![arXiv](https://img.shields.io/badge/arXiv-1708.07377-blue)](https://arxiv.org/abs/1708.07377)
+[![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+[![Python Versions](https://img.shields.io/pypi/pyversions/autolens)](https://pypi.org/project/autolens/)
+[![PyPI Version](https://img.shields.io/pypi/v/autolens.svg)](https://pypi.org/project/autolens/)
+
+[Installation Guide](https://pyautolens.readthedocs.io/en/latest/installation/overview.html) |
+[readthedocs](https://pyautolens.readthedocs.io/en/latest/index.html) |
+[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.4/start_here.ipynb) |
+[HowToLens](https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html)
+
+<img src="https://github.com/Jammy2211/PyAutoLogo/blob/main/gifs/pyautolens.gif?raw=true" width="900" />
+
+When two or more galaxies are aligned perfectly down our line-of-sight, the background galaxy appears multiple times.
+
+This is called strong gravitational lensing and **PyAutoLens** makes it **simple** to model strong gravitational lenses, using JAX to **accelerate lens modeling on GPUs**.
+
+## Getting Started
+
+The following links are useful for new starters:
+
+- [The PyAutoLens readthedocs](https://pyautolens.readthedocs.io/en/latest): which includes [an overview of PyAutoLens's core features](https://pyautolens.readthedocs.io/en/latest/overview/overview_1_start_here.html), [a new user starting guide](https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) and [an installation guide](https://pyautolens.readthedocs.io/en/latest/installation/overview.html).
+- [The introduction Jupyter Notebook on Google Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.4/start_here.ipynb): try **PyAutoLens** in a web browser (without installation).
+- [The autolens_workspace GitHub repository](https://github.com/PyAutoLabs/autolens_workspace): example scripts covering every **PyAutoLens** use case.
+- [The HowToLens GitHub repository](https://github.com/PyAutoLabs/HowToLens): a Jupyter notebook lecture series teaching strong lensing and lens modeling from the ground up.
+
+## Community & Support
+
+Support for **PyAutoLens** is available via our Slack workspace, where the community shares updates, discusses
+gravitational lensing 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](https://github.com/Jammy2211/PyAutoLens/issues).
+
+## HowToLens
+
+For users less familiar with gravitational lensing, Bayesian inference and scientific analysis
+you may wish to read through the **HowToLens** lectures. These teach you the basic principles of gravitational lensing
+and Bayesian inference, with the content pitched at undergraduate level and above.
+
+A complete overview of the lectures [is provided on the HowToLens readthedocs page](https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html), and the notebooks themselves live in the [PyAutoLabs/HowToLens](https://github.com/PyAutoLabs/HowToLens) repository.
+
+## Citations
+
+Information on how to cite **PyAutoLens** in publications can be found [on the citations page](https://github.com/Jammy2211/PyAutoLens/blob/main/CITATIONS.md).
+
+## Contributing
+
+Information on how to contribute to **PyAutoLens** can be found [on the contributing page](https://github.com/Jammy2211/PyAutoLens/blob/main/CONTRIBUTING.md).
+
+Hands on support for contributions is available via our Slack workspace, again please email to request an invite.

docs/conf.py

@@ -101,8 +101,7 @@
     ".DS_Store",
     "CODE_OF_CONDUCT.md",
     "CONTRIBUTING.md",
-    "CITATIONS.rst",
-    "README.rst",
+    "CITATIONS.md",
     "README.md",
 ]
 

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 name = "autolens"
 dynamic = ["version"]
 description="Open-Source Strong Lensing"
-readme = { file = "README.rst", content-type = "text/x-rst" }
+readme = { file = "README.md", content-type = "text/markdown" }
 license = { text = "MIT" }
 requires-python = ">=3.9"
 authors = [