Release 2026.8.22.1: bump Colab URL tag refs

Author: rhayes777

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/2026.8.20.1/start_here.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.8.22.1/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/PyAutoLabs/PyAutoLens/actions/workflows/main.yml/badge.svg)](https://github.com/PyAutoLabs/PyAutoLens/actions)
 [![Build](https://github.com/PyAutoLabs/PyAutoHands/actions/workflows/release.yml/badge.svg)](https://github.com/PyAutoLabs/PyAutoHands/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/2026.8.20.1/start_here.ipynb) |
+[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.8.22.1/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" />
@@ -34,7 +34,7 @@ The [**PyAutoLens AI Assistant**](https://github.com/PyAutoLabs/autolens_assista
 The following human-readable documentation and examples are also 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/2026.8.20.1/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/2026.8.22.1/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.
 

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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb)
+[Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb)
+[Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_2_ray_tracing.ipynb)
+[Tutorial 2: Ray Tracing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_3_more_ray_tracing.ipynb)
+[Tutorial 3: More Ray Tracing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_4_point_sources.ipynb)
+[Tutorial 4: Point Sources](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_1_introduction/tutorial_4_point_sources.ipynb)
 \- How lensing calculations change 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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_5_lensing_formalism.ipynb)
+[Tutorial 5: Lensing Formalism](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_6_data.ipynb)
+[Tutorial 6: Data](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_7_fitting.ipynb)
+[Tutorial 7: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_1_introduction/tutorial_8_summary.ipynb)
+[Tutorial 8: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_1_introduction/tutorial_8_summary.ipynb)
 \- A summary of the chapter.

docs/howtolens/chapter_2_lens_modeling.md

@@ -5,35 +5,35 @@ searches are chained together into automated lens modeling pipelines.
 
 The chapter contains the following tutorials:
 
-[Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/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/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_2_lens_modeling/tutorial_2_practicalities.ipynb)
+[Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/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/2026.8.22.1/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/2026.8.20.1/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/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb)
+[Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/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/2026.8.22.1/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/2026.8.20.1/notebooks/chapter_2_lens_modeling/tutorial_7_results.ipynb)
+[Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/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/2026.8.20.1/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/2026.8.22.1/notebooks/chapter_2_lens_modeling/tutorial_8_need_for_speed.ipynb)
 \- How to fit complex models whilst balancing efficiency and run-time.
 
-[Tutorial 9: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_2_lens_modeling/tutorial_9_search_chaining.ipynb)
+[Tutorial 9: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_2_lens_modeling/tutorial_9_search_chaining.ipynb)
 \- Breaking the lens modeling procedure into a chained sequence of model-fits.
 
-[Tutorial 10: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_2_lens_modeling/tutorial_10_prior_passing.ipynb)
+[Tutorial 10: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_2_lens_modeling/tutorial_10_prior_passing.ipynb)
 \- How the results of earlier searches are passed to later searches.
 
-[Tutorial 11: SLaM](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_2_lens_modeling/tutorial_11_slam.ipynb)
+[Tutorial 11: SLaM](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_2_lens_modeling/tutorial_11_slam.ipynb)
 \- Template pipelines for fitting lens models in standardized ways.

docs/howtolens/chapter_3_pixelizations.md

@@ -4,38 +4,38 @@ In chapter 3, 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/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_1_pixelizations.ipynb)
+[Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_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/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_2_mappers.ipynb)
+[Tutorial 2: Mappers](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_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/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_3_inversions.ipynb)
+[Tutorial 3: Inversions](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_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/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_4_bayesian_regularization.ipynb)
+[Tutorial 4: Bayesian Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_4_bayesian_regularization.ipynb)
 \- Smoothing the source within a Bayesian framework.
 
-[Tutorial 5: Bayesian Formalism](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_5_bayesian_formalism.ipynb)
+[Tutorial 5: Bayesian Formalism](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_5_bayesian_formalism.ipynb)
 \- The linear algebra and Bayesian evidence equations behind pixelized source reconstruction.
 
-[Tutorial 6: Borders](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_6_borders.ipynb)
+[Tutorial 6: Borders](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_6_borders.ipynb)
 \- Preventing highly demagnified image-pixels ruining the inversion.
 
-[Tutorial 7: Lens Modeling](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_7_lens_modeling.ipynb)
+[Tutorial 7: Lens Modeling](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_7_lens_modeling.ipynb)
 \- How to use inversions to fit a lens model.
 
-[Tutorial 8: Adaptive Pixelization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_8_adaptive_pixelization.ipynb)
+[Tutorial 8: Adaptive Pixelization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_8_adaptive_pixelization.ipynb)
 \- A Delaunay mesh which adapts to the mass model's magnification.
 
-[Tutorial 9: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_9_model_fit.ipynb)
+[Tutorial 9: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_9_model_fit.ipynb)
 \- An example lens modeling pipeline which uses an inversion.
 
-[Tutorial 10: Fit Problems](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_10_fit_problems.ipynb)
+[Tutorial 10: Fit Problems](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_10_fit_problems.ipynb)
 \- The shortcomings of our lens models and inversions.
 
-[Tutorial 11: Brightness Adaption](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_11_brightness_adaption.ipynb)
+[Tutorial 11: Brightness Adaption](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_11_brightness_adaption.ipynb)
 \- Adapting the pixelization to the source's morphology.
 
-[Tutorial 12: Adaptive Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_3_pixelizations/tutorial_12_adaptive_regularization.ipynb)
+[Tutorial 12: Adaptive Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_3_pixelizations/tutorial_12_adaptive_regularization.ipynb)
 \- Adapting the regularization to the source's morphology.

docs/howtolens/chapter_4_scaling_up_lensing.md

@@ -5,20 +5,20 @@ scaling relations, group and cluster scales, and weak lensing.
 
 The chapter contains the following tutorials:
 
-[Tutorial 1: Extra Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_4_scaling_up_lensing/tutorial_1_extra_galaxies.ipynb)
+[Tutorial 1: Extra Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_4_scaling_up_lensing/tutorial_1_extra_galaxies.ipynb)
 \- Handling nearby galaxies by scaling their light out of the fit or modeling them explicitly.
 
-[Tutorial 2: Multi Galaxy](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_4_scaling_up_lensing/tutorial_2_multi_galaxy.ipynb)
+[Tutorial 2: Multi Galaxy](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_4_scaling_up_lensing/tutorial_2_multi_galaxy.ipynb)
 \- Modeling lenses with two or more co-dominant lens galaxies and the degeneracies this creates.
 
-[Tutorial 3: Scaling Relation](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_4_scaling_up_lensing/tutorial_3_scaling_relation.ipynb)
+[Tutorial 3: Scaling Relation](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_4_scaling_up_lensing/tutorial_3_scaling_relation.ipynb)
 \- Tying galaxy masses to their luminosities so model complexity stops growing with each galaxy.
 
-[Tutorial 4: Group Scale](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_4_scaling_up_lensing/tutorial_4_group_scale.ipynb)
+[Tutorial 4: Group Scale](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_4_scaling_up_lensing/tutorial_4_group_scale.ipynb)
 \- Modeling group-scale lenses with truncated dPIE members and an optional group dark matter halo.
 
-[Tutorial 5: Cluster Scale](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_4_scaling_up_lensing/tutorial_5_cluster_scale.ipynb)
+[Tutorial 5: Cluster Scale](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_4_scaling_up_lensing/tutorial_5_cluster_scale.ipynb)
 \- Modeling cluster-scale lenses with point-source positions, the CSV interface and multi-plane ray tracing.
 
-[Tutorial 6: Weak Lensing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_4_scaling_up_lensing/tutorial_6_weak_lensing.ipynb)
+[Tutorial 6: Weak Lensing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_4_scaling_up_lensing/tutorial_6_weak_lensing.ipynb)
 \- Fitting weak-lensing shear catalogues, which probe mass far beyond the strong lensing region.

docs/howtolens/chapter_optional.md

@@ -4,5 +4,5 @@ This chapter contains optional tutorials expanding on different aspects of how *
 
 The chapter contains the following tutorials:
 
-[Tutorial: Searches](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.20.1/notebooks/chapter_optional/tutorial_searches.ipynb)
+[Tutorial: Searches](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.22.1/notebooks/chapter_optional/tutorial_searches.ipynb)
 \- Alternative non-linear searches to sample parameter space.