docs: scaffold PyAutoLens-JAX JOSS paper

[Jammy2211]

Overview

Create a second, current-format JOSS manuscript for PyAutoLens-JAX beside the published PyAutoLens paper. The scaffold will capture the differentiable, GPU-accelerated strong- and weak-lensing scope while preserving the existing paper unchanged.

Plan

• Add a sibling paper_jax/ manuscript directory.
• Use the exact approved title and supplied summary.
• Follow current JOSS metadata and required-section conventions.
• Seed a focused bibliography and drafting/build guidance.
• Validate the manuscript structure and compile it with JOSS tooling where available.

Detailed implementation plan

Affected Repositories

• PyAutoLens (primary)

Branch Survey

Repository	Current Branch	Dirty?
./PyAutoLens	main	clean

Suggested branch: feature/pyautolens-jax-joss-paper

Work Classification: Library

Worktree root: ~/Code/PyAutoLabs-wt/pyautolens-jax-joss-paper/

Implementation Steps

1. Add paper_jax/paper.md with current JOSS YAML, the exact title, supplied summary, required sections, and explicit drafting placeholders.
2. Add paper_jax/paper.bib seeded with verified PyAutoLens and JAX references.
3. Add paper_jax/README.md with scope, drafting checklist, and Inara build instructions.
4. Validate metadata, citations, structure, and PDF compilation without changing paper/.

Key Files

• paper_jax/paper.md — JOSS manuscript template and supplied summary.
• paper_jax/paper.bib — focused starter bibliography.
• paper_jax/README.md — drafting checklist and compilation instructions.

Verification

• Confirm the original paper/ tree is unchanged.
• Validate the YAML front matter and all cited BibTeX keys.
• Compile with the official openjournals/inara image when Docker is available.

Original Prompt

Click to expand starting prompt

Set up the PyAutoLens-JAX JOSS paper

Type: docs
Target: PyAutoLens
Repos:

• PyAutoLens
  Difficulty: small
  Autonomy: supervised
  Priority: normal
  Status: formalised

Request

Create a second JOSS paper template alongside the existing PyAutoLens/paper
directory. Give the new paper the title:

PyAutoLens-JAX: Differentiable GPU-accelerated strong and weak lensing from galaxies to clusters

Preserve the existing paper and follow its repository-local JOSS structure and
conventions where appropriate.

Summary draft supplied by the author

Gravitational lensing probes luminous and dark matter from individual galaxies to groups and clusters, using observations that increasingly provide multiple complementary forms of information. A single system may include multi-band optical or infrared imaging, radio interferometer visibilities, point-source constraints from lensed quasars or supernovae, and weak-lensing shear measurements. Fully exploiting modern lensing datasets therefore warrants joint probabilistic modelling across physical scales, lensing regimes, and 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 strong lensing, weak lensing, CCD imaging, interferometer visibilities, and point-source observables. Crucially, these are not isolated capabilities: users can combine any number of datasets, lensing regimes, lens planes, and physical scales within a single differentiable, GPU-accelerated probabilistic model.

Original request verbatim

ok, we are now writing another PyAutoLEens JOSS paper whiuch can go side by side with the paper in PyAutoLens/paper, can you set up a template in the repo and make the title PyAutoLens-JAX: Differentiable GPU-accelerated strong and weak lensing from galaxies to clusters

[Jammy2211]

Progress Update — 2026-07-14

Summary

The PyAutoLens-JAX JOSS scaffold is implemented in the task worktree on feature/pyautolens-jax-joss-paper. It adds a current-format sibling paper_jax/ directory, preserves the published paper/ tree unchanged, and includes the author's revised 150-word Summary verbatim.

Changes

• paper_jax/paper.md — exact approved title, current JOSS metadata and required sections, revised Summary, drafting prompts, and AI usage disclosure.
• paper_jax/paper.bib — verified starter citations for PyAutoLens and JAX.
• paper_jax/README.md — drafting checklist and official Inara build command.
• paper_jax/.gitignore — excludes the generated paper.pdf.

Validation

• YAML title and bibliography metadata: pass.
• Current required JOSS sections: 8/8 present.
• Citation keys: 2/2 resolved.
• Existing paper/: unchanged.
• Official Inara PDF build: not run because Docker is unavailable in this environment.

Remaining

• Rerun ship_library when Heart is no longer RED.
• Commit, push, and open the pending-release PR.

Ship blocker

Heart currently reports RED for two unrelated reasons:

• PyAutoArray: on branch feature/ticks-minus-in-math (not main)
• release validation FAILED (stage integrate)

This documentation task is not eligible for the corrective-PR exception, so no commit, push, or PR was created.

[Jammy2211]

Smoke Test Results — 2026-07-14

Workspace	Passed	Failed	Skipped	Total	Duration
autofit_workspace	9	0	1	10	16.5s
autogalaxy_workspace	8	0	0	8	56.3s
autolens_workspace	9	0	0	9	74.9s
autolens_workspace_test	15	0	1	16	153.0s
euclid_strong_lens_modeling_pipeline	6	0	0	6	153.3s
HowToLens	6	0	0	6	10.6s

Total: 53 passed, 0 failed, 2 skipped, 55 entries across six workspaces.

[Jammy2211]

Shipped

Merged PR #609 into main at eab44c046730bba72442b7c57896a1bf6bedad56.

The new paper_jax/ JOSS manuscript scaffold contains the approved title and draft Summary, Statement of need, and benchmark material, plus the current JOSS-required section structure and AI disclosure.

Validation: 381 PyAutoLens tests passed; all four GitHub test jobs passed; downstream smoke gate passed 53 checks across six workspaces with 0 failures and 2 configured skips.