docs: scaffold PyAutoLens-JAX JOSS paper

paper_jax/.gitignore

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+/paper.pdf

paper_jax/README.md

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+# PyAutoLens-JAX JOSS paper
+
+This directory contains the second PyAutoLens paper for submission to the
+[Journal of Open Source Software](https://joss.theoj.org/). It is intentionally
+separate from `../paper/`, which contains the published 2021 PyAutoLens paper.
+
+## Files
+
+- `paper.md` — manuscript and JOSS metadata.
+- `paper.bib` — bibliography cited by the manuscript.
+- `paper.pdf` — local build output; do not commit it.
+
+## Drafting checklist
+
+- Confirm the full author list, affiliations, ORCIDs, corresponding author, and
+  submission date.
+- Expand the manuscript to the current JOSS target of 750–1750 words.
+- Replace every drafting comment with specific, evidenced prose.
+- Compare against relevant strong- and weak-lensing software in “State of the
+  field”.
+- Include reproducible GPU benchmarks and distinguish compilation from
+  steady-state execution.
+- Add concrete evidence of research impact and verify every bibliography entry.
+- Keep the AI usage disclosure accurate as the manuscript evolves.
+
+The current format requirements are documented in the
+[JOSS paper guide](https://joss.readthedocs.io/en/latest/paper.html).
+
+## Build the paper
+
+From the PyAutoLens repository root, compile with the official JOSS Inara image:
+
+```bash
+docker run --rm \
+  --volume "$PWD/paper_jax:/data" \
+  --user "$(id -u):$(id -g)" \
+  --env JOURNAL=joss \
+  openjournals/inara
+```
+
+The generated PDF is written to `paper_jax/paper.pdf`.

paper_jax/paper.bib

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+@article{Nightingale2021,
+  doi = {10.21105/joss.02825},
+  url = {https://doi.org/10.21105/joss.02825},
+  year = {2021},
+  publisher = {The Open Journal},
+  volume = {6},
+  number = {58},
+  pages = {2825},
+  author = {Nightingale, James W. and Hayes, Richard G. and Kelly, Ashley and Amvrosiadis, Aristeidis and Etherington, Amy and He, Qiuhan and Li, Nan and Cao, XiaoYue and Frawley, Jonathan and Cole, Shaun and Enia, Andrea and Frenk, Carlos S. and Harvey, David R. and Li, Ran and Massey, Richard J. and Negrello, Mattia and Robertson, Andrew},
+  title = {{PyAutoLens}: Open-Source Strong Gravitational Lensing},
+  journal = {Journal of Open Source Software}
+}
+
+@software{jax2018github,
+  author = {Bradbury, James and Frostig, Roy and Hawkins, Peter and Johnson, Matthew James and Leary, Chris and Maclaurin, Dougal and Necula, George and Paszke, Adam and VanderPlas, Jake and Wanderman-Milne, Skye and Zhang, Qiao},
+  title = {{JAX}: composable transformations of {Python}+{NumPy} programs},
+  url = {https://github.com/jax-ml/jax},
+  year = {2018}
+}

paper_jax/paper.md

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+---
+title: "PyAutoLens-JAX: Differentiable GPU-accelerated strong and weak lensing from galaxies to clusters"
+tags:
+  - Python
+  - astronomy
+  - gravitational lensing
+  - automatic differentiation
+  - GPU acceleration
+authors:
+  - name: James W. Nightingale
+    orcid: 0000-0002-8987-7401
+    affiliation: 1
+    corresponding: true
+affiliations:
+  - name: Institute for Computational Cosmology, Durham University, United Kingdom
+    index: 1
+date: 14 July 2026
+bibliography: paper.bib
+---
+
+# Summary
+
+Gravitational lensing probes luminous and dark matter across galaxy-, group-, and cluster-scale systems using observations that increasingly provide multiple complementary forms of information. A single system may include strong- and weak-lensing constraints, multi-band optical or infrared imaging, radio interferometer visibilities, and point-source measurements from lensed quasars or supernovae. Fully exploiting modern lensing datasets therefore warrants joint probabilistic modelling across galaxy, group, and cluster scales, combining strong and weak lensing with diverse observational data types.
+
+PyAutoLens is now implemented using JAX throughout its core modelling framework, providing just-in-time compilation, GPU acceleration, and automatic differentiation without introducing a separate package or replacing its established object-oriented API. Galaxy-, group-, and cluster-scale mass models can be constrained using CCD imaging, interferometer visibilities, point-source observables, and weak-lensing catalogues. Crucially, these are not isolated capabilities: users can combine multiple datasets, strong- and weak-lensing constraints, lens planes, and mass components within a single differentiable, GPU-accelerated probabilistic model.
+
+# Statement of need
+
+The number of known gravitational lenses is increasing rapidly as wide-field surveys discover large samples of galaxy-, group-, and cluster-scale systems. At the same time, the information available for each lens is becoming richer. High-resolution imaging constrains extended arcs and lens-galaxy light, interferometer observations probe source structure in the visibility domain, point-source measurements constrain image positions and time delays, and weak lensing probes mass on larger spatial scales. Group and cluster lenses further introduce multiple deflectors, multiple source planes, and more complex mass distributions. Jointly modelling these observables can break degeneracies and provide more complete physical constraints, but it also creates increasingly expensive and high-dimensional likelihood functions.
+
+Conventional derivative-free inference becomes difficult as analyses combine pixelized source reconstructions, multi-band datasets, millions of interferometer visibilities, strong- and weak-lensing constraints, and multi-scale mass models across increasingly large lens samples. PyAutoLens-JAX addresses this computational bottleneck by making the complete modelling framework compatible with GPU execution and automatic differentiation. This enables faster likelihood evaluation and the use of gradient-based optimisation and sampling methods across the full range of PyAutoLens datasets and lensing regimes.
+
+# State of the field
+
+<!--
+Position PyAutoLens-JAX relative to the published PyAutoLens software
+[@Nightingale2021] and other current strong- and weak-lensing packages. Focus on
+the combination of established modelling abstractions, joint multi-dataset
+inference, automatic differentiation, and GPU execution.
+-->
+
+# Software design
+
+## Differentiable modelling
+
+<!--
+Describe the JAX-compatible numerical backend, just-in-time compilation,
+automatic differentiation, and preservation of the public PyAutoLens API. Cite
+JAX [@jax2018github] and explain which model components are differentiable.
+-->
+
+## Joint datasets and lensing regimes
+
+<!--
+Describe how analyses combine imaging, interferometer, point-source, and weak-
+lensing observations across lens planes and physical scales in one likelihood.
+Include a concise representative example or schematic.
+-->
+
+## End-to-end modelling benchmarks
+
+All benchmarks use gradient-based inference with JAX automatic differentiation and are executed on an NVIDIA A100 GPU. Unless otherwise stated, the lens model comprises a singular isothermal ellipsoid with external shear, a multi-Gaussian expansion for the lens light, and a pixelized reconstruction of the lensed source. For every analysis, we report the total wall-clock time, JAX compilation time, number of likelihood evaluations, and post-compilation runtime.
+
+### Single-dataset and single-regime benchmarks
+
+These benchmarks establish GPU acceleration and automatic differentiation across the individual lensing scales and observational data types supported by PyAutoLens-JAX.
+
+- **Galaxy-scale CCD imaging:** Model JWST COSMOS-Web Ring F150W imaging, including lens-light subtraction and a pixelized source reconstruction, in approximately five minutes.
+- **Interferometry:** Model a real ALMA strong-lensing dataset containing more than one million interferometer visibilities in approximately five minutes.
+- **Point-source lensing:** Model a real multiply imaged quasar or supernova using point-source observables, including image positions and, where available, time delays or flux information, in under five minutes.
+- **Group-scale strong lensing:** Model a real group-scale lens containing multiple deflecting galaxies in under five minutes, demonstrating that PyAutoLens-JAX is not restricted to isolated galaxy-scale lenses.
+- **Cluster-scale strong lensing:** Model a real cluster lens with multiple mass components, multiple images, and potentially multiple source planes in under five minutes.
+- **Weak lensing:** Fit a weak-lensing shear catalogue using a differentiable JAX likelihood in under five minutes, demonstrating that PyAutoLens-JAX is not restricted to strong-lensing data.
+
+### Joint and multi-dataset benchmarks
+
+These benchmarks demonstrate the central capability of PyAutoLens-JAX: different datasets, lensing regimes, and physical scales can be combined within a single differentiable, GPU-accelerated probabilistic model.
+
+- **Multi-band imaging:** Jointly model the four available JWST COSMOS-Web Ring bands, constraining a common lens mass model while fitting the wavelength-dependent lens and source emission in each dataset.
+- **Joint strong and weak lensing:** Constrain a single group- or cluster-scale mass model using both strong-lensing and weak-lensing observables.
+- **Imaging and point-source lensing:** Jointly model extended arcs and point-source constraints from a lensed quasar or supernova within the same lens model.
+- **Imaging and interferometry:** Jointly fit optical or infrared imaging and radio or submillimetre interferometer visibilities, constraining a common mass model using complementary observations of the lensed source.
+
+The single examples verify that each major PyAutoLens likelihood and modelling regime is JAX compatible. The combined examples then demonstrate that these capabilities are not implemented as isolated workflows: they can be composed while retaining a common object-oriented API, automatic differentiation, and GPU execution.
+
+# Research impact statement
+
+<!--
+Give specific evidence of research enabled by PyAutoLens and the JAX framework:
+published or ongoing projects, external adoption, survey-scale applications,
+and reproducible examples. Keep this about software impact rather than new
+scientific results.
+-->
+
+# AI usage disclosure
+
+Generative AI tools were used to scaffold this manuscript template and may be used to assist drafting. All scientific claims, citations, and prose are reviewed and verified by the authors.
+
+# Acknowledgements
+
+<!-- List funding, facilities, software contributors, and other support. -->
+
+# References