Release 2026.5.8.2: bump Colab URL tag refs

rhayes777 · rhayes777 · commit 04bd32229b67 · 2026-05-08T19:07:52.000Z
diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology
 
-[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.1/start_here.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.2/start_here.ipynb)
 [![Documentation Status](https://readthedocs.org/projects/pyautogalaxy/badge/?version=latest)](https://pyautogalaxy.readthedocs.io/en/latest/?badge=latest)
 [![Tests](https://github.com/Jammy2211/PyAutoGalaxy/actions/workflows/main.yml/badge.svg)](https://github.com/Jammy2211/PyAutoGalaxy/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://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html) |
 [readthedocs](https://pyautogalaxy.readthedocs.io/en/latest/index.html) |
-[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.1/start_here.ipynb) |
+[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.2/start_here.ipynb) |
 [HowToGalaxy](https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html)
 
 **PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies:
@@ -30,7 +30,7 @@
 The following links are useful for new starters:
 
 - [The PyAutoGalaxy readthedocs](https://pyautogalaxy.readthedocs.io/en/latest), which includes [an overview of PyAutoGalaxy's core features](https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_1_start_here.html), [a new user starting guide](https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) and [an installation guide](https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html).
-- [The introduction Jupyter Notebook on Google Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.1/start_here.ipynb), where you can try **PyAutoGalaxy** in a web browser (without installation).
+- [The introduction Jupyter Notebook on Google Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.2/start_here.ipynb), where you can try **PyAutoGalaxy** in a web browser (without installation).
 - [The autogalaxy_workspace GitHub repository](https://github.com/PyAutoLabs/autogalaxy_workspace): example scripts covering every **PyAutoGalaxy** use case.
 - [The HowToGalaxy GitHub repository](https://github.com/PyAutoLabs/HowToGalaxy): a Jupyter notebook lecture series teaching galaxy modeling from the ground up.
 
diff --git a/docs/howtogalaxy/chapter_1_introduction.md b/docs/howtogalaxy/chapter_1_introduction.md
@@ -6,20 +6,20 @@ In chapter 1, we introduce you to galaxy morphology, structure and the core **Py
 
 The chapter contains the following tutorials:
 
-[Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb)
+[Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb)
 \- Setting up **PyAutoGalaxy**'s visualization library.
 
-[Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb)
+[Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/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: Data](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_2_data.ipynb)
+[Tutorial 2: Data](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_1_introduction/tutorial_2_data.ipynb)
 \- Simulating and inspecting telescope imaging data of a galaxy.
 
-[Tutorial 3: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb)
+[Tutorial 3: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb)
 \- Fitting data with a galaxy model.
 
-[Tutorial 4: Methods](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb)
+[Tutorial 4: Methods](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb)
 \- An overview of the different methods used to fit galaxies with.
 
-[Tutorial 5: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb)
+[Tutorial 5: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb)
 \- A summary of the chapter.
diff --git a/docs/howtogalaxy/chapter_2_modeling.md b/docs/howtogalaxy/chapter_2_modeling.md
@@ -4,26 +4,26 @@ In chapter 2, we'll take you through how to model galaxies using a non-linear se
 
 The chapter contains the following tutorials:
 
-[Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_2_modeling/tutorial_1_non_linear_search.ipynb)
+[Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_2_modeling/tutorial_1_non_linear_search.ipynb)
 \- How a non-linear search is used to fit a model and the concepts of a parameter space and priors.
 
-[Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_2_modeling/tutorial_2_practicalities.ipynb)
+[Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_2_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/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb)
+[Tutorial 3: Realism and Complexity](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb)
 \- Finding a balance between realism and complexity when composing and fitting a model.
 
-[Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb)
+[Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb)
 \- What to do when PyAutoGalaxy finds an inaccurate model.
 
-[Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb)
+[Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb)
 \- Light profiles which capture complex morphologies in a reduced number of non-linear parameters.
 
-[Tutorial 6: Masking](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb)
+[Tutorial 6: Masking](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb)
 \- How to mask your data to improve the model.
 
-[Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_2_modeling/tutorial_7_results.ipynb)
+[Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_2_modeling/tutorial_7_results.ipynb)
 \- Overview of the results available after successfully fitting a model.
 
-[Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb)
+[Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb)
 \- How to fit complex models whilst balancing efficiency and run-time.
diff --git a/docs/howtogalaxy/chapter_3_search_chaining.md b/docs/howtogalaxy/chapter_3_search_chaining.md
@@ -5,11 +5,11 @@ robust modeling of large galaxy samples.
 
 The chapter contains the following tutorials:
 
-[Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb)
+[Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb)
 \- Breaking the modeling procedure into a chained sequence of model-fits.
 
-[Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb)
+[Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb)
 \- How the results of earlier searches are passed to later searches.
 
-[Tutorial 3: x2 Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb)
+[Tutorial 3: x2 Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb)
 \- Modeling a dataset with two galaxies using chained searches.
diff --git a/docs/howtogalaxy/chapter_4_pixelizations.md b/docs/howtogalaxy/chapter_4_pixelizations.md
@@ -4,17 +4,17 @@ In chapter 4, we use **Pixelizations** to reconstruct complex galaxies on pixeli
 
 The chapter contains the following tutorials:
 
-[Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb)
+[Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb)
 \- Creating a pixel-grid which will reconstruct a galaxy's light.
 
-[Tutorial 2: Mappers](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb)
+[Tutorial 2: Mappers](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb)
 \- How a pixelization maps between the data and pixelization.
 
-[Tutorial 3: Inversions](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb)
+[Tutorial 3: Inversions](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb)
 \- Inverting the mappings to reconstruct the galaxy's light.
 
-[Tutorial 4: Bayesian Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb)
+[Tutorial 4: Bayesian Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb)
 \- Smoothing the source within a Bayesian framework.
 
-[Tutorial 6: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_4_pixelizations/tutorial_6_model_fit.ipynb)
+[Tutorial 6: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_4_pixelizations/tutorial_6_model_fit.ipynb)
 \- An example modeling pipeline which uses an inversion.
diff --git a/docs/howtogalaxy/chapter_optional.md b/docs/howtogalaxy/chapter_optional.md
@@ -4,11 +4,11 @@ This chapter contains optional tutorials expanding on different aspects of how *
 
 The chapter contains the following tutorials:
 
-[Tutorial: Mass Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_optional/tutorial_mass_profiles.ipynb)
+[Tutorial: Mass Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_optional/tutorial_mass_profiles.ipynb)
 \- A description of mass profiles implemented in PyAutoGalaxy, which are currently only used by its child project PyAutoLens.
 
-[Tutorial: Sub-grids](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_optional/tutorial_sub_grids.ipynb)
+[Tutorial: Sub-grids](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_optional/tutorial_sub_grids.ipynb)
 \- Use sub-grids to perform more accurate and precise calculations.
 
-[Tutorial: Searches](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_optional/tutorial_searches.ipynb)
+[Tutorial: Searches](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_optional/tutorial_searches.ipynb)
 \- Alternative non-linear searches to sample parameter space.
diff --git a/docs/index.md b/docs/index.md
@@ -23,7 +23,7 @@ substitutions:
 The following links are useful for new starters:
 
 - [The PyAutoGalaxy readthedocs](https://pyautogalaxy.readthedocs.io/en/latest/), which includes [an overview of PyAutoGalaxy's core features](https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_1_start_here.html), [a new user starting guide](https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) and [an installation guide](https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html).
-- [The introduction Jupyter Notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.1/start_here.ipynb), where you can try **PyAutoGalaxy** in a web browser (without installation).
+- [The introduction Jupyter Notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.2/start_here.ipynb), where you can try **PyAutoGalaxy** in a web browser (without installation).
 - [The autogalaxy_workspace GitHub repository](https://github.com/PyAutoLabs/autogalaxy_workspace): example scripts covering every **PyAutoGalaxy** use case.
 - [The HowToGalaxy GitHub repository](https://github.com/PyAutoLabs/HowToGalaxy): a Jupyter notebook lecture series teaching galaxy 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
@@ -24,11 +24,11 @@ environment with all the required dependencies already installed.
 This is a great way to get started quickly without needing to install **PyAutoGalaxy** on your own machine,
 so you can check it is the right software for you before going through the installation process:
 
-- [imaging/start_here.ipynb](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.1/notebooks/imaging/start_here.ipynb):
+- [imaging/start_here.ipynb](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.2/notebooks/imaging/start_here.ipynb):
   Galaxy modeling with CCD imaging (e.g. Hubble, James Webb, ground-based telescopes).
-- [interferometer/start_here.ipynb](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.1/notebooks/interferometer/start_here.ipynb):
+- [interferometer/start_here.ipynb](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.2/notebooks/interferometer/start_here.ipynb):
   Galaxy modeling with interferometer data (e.g. ALMA), fitting directly in the uv-plane.
-- [multi_band/start_here.ipynb](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.1/notebooks/multi/start_here.ipynb):
+- [multi_band/start_here.ipynb](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.2/notebooks/multi/start_here.ipynb):
   Multi-band galaxy modeling to study colour gradients and wavelength-dependent structure.
 
 ## Still Unsure?
diff --git a/paper/paper.md b/paper/paper.md
@@ -75,7 +75,7 @@ massively parallel model-fitting and an SQLite3 database that allows large suite
 queried and analysed. Accompanying `PyAutoGalaxy` is the [autogalaxy workspace](https://github.com/PyAutoLabs/autogalaxy_workspace),
 which includes example scripts and galaxy datasets covering every use case. The [`HowToGalaxy`](https://github.com/PyAutoLabs/HowToGalaxy)
 repository provides a separate Jupyter notebook lecture series which introduces non-experts to galaxy morphology studies using `PyAutoGalaxy`. Readers can try `PyAutoGalaxy` right now by going 
-to [the introduction Jupyter notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.1/start_here.ipynb) or 
+to [the introduction Jupyter notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.8.2/start_here.ipynb) or 
 checkout the [readthedocs](https://pyautogalaxy.readthedocs.io/en/latest/) for a complete overview of `PyAutoGalaxy`'s 
 features.
 
@@ -157,7 +157,7 @@ interferometer datasets before a `PyAutoGalaxy` analysis. The [`HowToGalaxy`](ht
 tutorials — a standalone repository separate from the workspace — are a four-chapter lecture series composed of over
 20 Jupyter notebooks aimed at non-experts, introducing them to galaxy morphology analysis, Bayesian inference and
 teaching them how to use `PyAutoGalaxy` for scientific study. The lectures are available on
-[Colab](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb)
+[Colab](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.5.8.2/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb)
 and may therefore be taken without a local `PyAutoGalaxy` installation.
 
 # Software Citations
