Release 2026.5.8.1: bump Colab URL tag refs

rhayes777 · rhayes777 · commit 1297fb2f545f · 2026-05-08T18:21:12.000Z
diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # 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)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.8.1/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)
@@ -14,7 +14,7 @@
 
 [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) |
+[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.8.1/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" />
@@ -28,7 +28,7 @@ This is called strong gravitational lensing and **PyAutoLens** makes it **simple
 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 introduction Jupyter Notebook on Google Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.8.1/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.
 
diff --git a/docs/howtolens/chapter_1_introduction.md b/docs/howtolens/chapter_1_introduction.md
@@ -6,29 +6,29 @@ In chapter 1, we introduce you to strong gravitational lensing and the core **Py
 
 The chapter contains the following tutorials:
 
-[Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb)
+[Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb)
 \- Setting up **PyAutoLens**'s visualization library.
 
-[Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb)
+[Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb)
 \- Using grids of (y,x) coordinates with galaxies made up of light profiles.
 
-[Tutorial 2: Ray Tracing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_2_ray_tracing.ipynb)
+[Tutorial 2: Ray Tracing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_2_ray_tracing.ipynb)
 \- Using grids, galaxies and mass profiles to perform strong lens ray-tracing.
 
-[Tutorial 3: More Ray Tracing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_3_more_ray_tracing.ipynb)
+[Tutorial 3: More Ray Tracing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_3_more_ray_tracing.ipynb)
 \- Advanced strong lens ray-tracing.
 
-[Tutorial 4: Point Sources](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_4_point_sources.ipynb)
+[Tutorial 4: Point Sources](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_4_point_sources.ipynb)
 \- How lensing calculations when the source galaxy is a point-source (e.g. a quasar).
 
-[Tutorial 5: Lensing Formalism](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_5_lensing_formalism.ipynb)
+[Tutorial 5: Lensing Formalism](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_5_lensing_formalism.ipynb)
 \- The algebraic lensing formalism used to describe strong lensing.
 
-[Tutorial 6: Data](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_6_data.ipynb)
+[Tutorial 6: Data](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_6_data.ipynb)
 \- Loading and inspecting telescope imaging data of a strong lens.
 
-[Tutorial 7: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_7_fitting.ipynb)
+[Tutorial 7: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_7_fitting.ipynb)
 \- Fitting data with a strong lens model.
 
-[Tutorial 8: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_8_summary.ipynb)
+[Tutorial 8: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_8_summary.ipynb)
 \- A summary of the chapter.
diff --git a/docs/howtolens/chapter_2_lens_modeling.md b/docs/howtolens/chapter_2_lens_modeling.md
@@ -4,26 +4,26 @@ In chapter 2, we'll take you through how to model strong lenses using a non-line
 
 The chapter contains the following tutorials:
 
-[Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_1_non_linear_search.ipynb)
+[Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_2_lens_modeling/tutorial_1_non_linear_search.ipynb)
 \- How a non-linear search is used to fit a lens model and the concepts of a parameter space and priors.
 
-[Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_2_practicalities.ipynb)
+[Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_2_lens_modeling/tutorial_2_practicalities.ipynb)
 \- Practicalities of performing model-fitting, like how to inspect the results on your hard-disk.
 
-[Tutorial 3: Realism and Complexity](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.ipynb)
+[Tutorial 3: Realism and Complexity](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.ipynb)
 \- Finding a balance between realism and complexity when composing and fitting a lens model.
 
-[Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.ipynb)
+[Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.ipynb)
 \- What to do when PyAutoLens finds an inaccurate lens model.
 
-[Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb)
+[Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb)
 \- Light profiles which capture complex morphologies in a reduced number of non-linear parameters.
 
-[Tutorial 6: Masking and Positions](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_6_masking_and_positions.ipynb)
+[Tutorial 6: Masking and Positions](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_2_lens_modeling/tutorial_6_masking_and_positions.ipynb)
 \- How to mask and mark positions on your data to improve the lens model.
 
-[Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_7_results.ipynb)
+[Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_2_lens_modeling/tutorial_7_results.ipynb)
 \- Overview of the results available after successfully fitting a lens model.
 
-[Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_8_need_for_speed.ipynb)
+[Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_2_lens_modeling/tutorial_8_need_for_speed.ipynb)
 \- How to fit complex models whilst balancing efficiency and run-time.
diff --git a/docs/howtolens/chapter_3_search_chaining.md b/docs/howtolens/chapter_3_search_chaining.md
@@ -5,20 +5,20 @@ robust modeling of large strong lens samples.
 
 The chapter contains the following tutorials:
 
-[Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb)
+[Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb)
 \- Breaking the lens modeling procedure into a chained sequence of model-fits.
 
-[Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb)
+[Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb)
 \- How the results of earlier searches are passed to later searches.
 
-[Tutorial 3: Lens and Source](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_3_lens_and_source.ipynb)
+[Tutorial 3: Lens and Source](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_3_lens_and_source.ipynb)
 \- Fitting the lens's light followed by its mass using chained searches.
 
-[Tutorial 4: Two Lens galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.ipynb)
+[Tutorial 4: Two Lens galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.ipynb)
 \- Modeling a strong lens with two lens galaxies using chained searches.
 
-[Tutorial 5: Complex Source](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_4_complex_source.ipynb)
+[Tutorial 5: Complex Source](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_4_complex_source.ipynb)
 \- Using multiple light profiles to fit a complex and irregular source using chained searches.
 
-[Tutorial 6: SLaM](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_6_slam.ipynb)
+[Tutorial 6: SLaM](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_6_slam.ipynb)
 \- Template pipelines for fitting lens model is standardized ways.
diff --git a/docs/howtolens/chapter_4_pixelizations.md b/docs/howtolens/chapter_4_pixelizations.md
@@ -4,35 +4,35 @@ In chapter 4, we use **Pixelizations** to reconstruct complex source galaxies on
 
 The chapter contains the following tutorials:
 
-[Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb)
+[Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb)
 \- Creating a pixel-grid in the source-plane.
 
-[Tutorial 2: Mappers](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb)
+[Tutorial 2: Mappers](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb)
 \- How a pixelization maps source-pixels to image-pixels.
 
-[Tutorial 3: Inversions](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb)
+[Tutorial 3: Inversions](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb)
 \- Inverting the mappings to reconstruct the source's light.
 
-[Tutorial 4: Bayesian Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb)
+[Tutorial 4: Bayesian Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb)
 \- Smoothing the source within a Bayesian framework.
 
-[Tutorial 5: Borders](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_5_borders.ipynb)
+[Tutorial 5: Borders](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_5_borders.ipynb)
 \- Preventing highly demagnified image-pixels ruining the inversion.
 
-[Tutorial 6: Lens Modeling](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_6_lens_modeling.ipynb)
+[Tutorial 6: Lens Modeling](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_6_lens_modeling.ipynb)
 \- How to use inversions to fit a lens model.
 
-[Tutorial 7: Adaptive Pixelization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.ipynb)
+[Tutorial 7: Adaptive Pixelization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.ipynb)
 \- A Voronoi mesh which adapts to the mass model's magnification.
 
-[Tutorial 8: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_8_model_fit.ipynb)
+[Tutorial 8: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_8_model_fit.ipynb)
 \- An example lens modeling pipeline which uses an inversion.
 
-[Tutorial 9: Fit Problems](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_9_fit_problems.ipynb)
+[Tutorial 9: Fit Problems](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_9_fit_problems.ipynb)
 \- The shortcomings of our lens models and inversions.
 
-[Tutorial 10: Brightness Adaption](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_10_brightness_adaption.ipynb)
+[Tutorial 10: Brightness Adaption](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_10_brightness_adaption.ipynb)
 \- Adapting the pixelization to the source's morphology.
 
-[Tutorial 11: Adaptive Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_11_adapt_regularization.py.ipynb)
+[Tutorial 11: Adaptive Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_11_adapt_regularization.py.ipynb)
 \- Adapting the regularization to the source's morphology.
diff --git a/docs/howtolens/chapter_optional.md b/docs/howtolens/chapter_optional.md
@@ -4,8 +4,8 @@ This chapter contains optional tutorials expanding on different aspects of how *
 
 The chapter contains the following tutorials:
 
-[Tutorial: Sub-grids](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_optional/tutorial_sub_grids.ipynb)
+[Tutorial: Sub-grids](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_optional/tutorial_sub_grids.ipynb)
 \- Use sub-grids to perform more accuratee and precise lensing calculations.
 
-[Tutorial: Searches](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_optional/tutorial_searches.ipynb)
+[Tutorial: Searches](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.8.1/notebooks/chapter_optional/tutorial_searches.ipynb)
 \- Alternative non-linear searches to sample parameter space.
diff --git a/docs/index.md b/docs/index.md
@@ -13,7 +13,7 @@ This is called strong gravitational lensing and **PyAutoLens** makes it simple t
 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 Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.4/start_here.ipynb), where you can try **PyAutoLens** in a web browser (without installation).
+- [The introduction Jupyter Notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.8.1/start_here.ipynb), where you can try **PyAutoLens** in a web browser (without installation).
 - [The autolens_workspace GitHub repository](https://github.com/PyAutoLabs/autolens_workspace), which includes 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.
 
diff --git a/docs/overview/overview_2_new_user_guide.md b/docs/overview/overview_2_new_user_guide.md
diff --git a/paper/paper.md b/paper/paper.md
