Merge pull request #690 from PyAutoLabs/claude/howtolens-tutorial-restructure-f19y9d

Update RTD HowToLens pages for the tutorial restructure

Author: Jammy2211

docs/howtolens/chapter_1_introduction.md

@@ -19,7 +19,7 @@ The chapter contains the following tutorials:
 \- Advanced strong lens ray-tracing.
 
 [Tutorial 4: Point Sources](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.4.1/notebooks/chapter_1_introduction/tutorial_4_point_sources.ipynb)
-\- How lensing calculations when the source galaxy is a point-source (e.g. a quasar).
+\- 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/2026.8.4.1/notebooks/chapter_1_introduction/tutorial_5_lensing_formalism.ipynb)
 \- The algebraic lensing formalism used to describe strong lensing.

docs/howtolens/chapter_2_lens_modeling.md

@@ -1,6 +1,7 @@
 # Chapter 2: Lens Modeling
 
-In chapter 2, we'll take you through how to model strong lenses using a non-linear search.
+In chapter 2, we'll take you through how to model strong lenses using a non-linear search, ending with how
+searches are chained together into automated lens modeling pipelines.
 
 The chapter contains the following tutorials:
 
@@ -27,3 +28,12 @@ The chapter contains the following tutorials:
 
 [Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/notebooks/chapter_2_lens_modeling/tutorial_11_slam.ipynb)
+\- Template pipelines for fitting lens models in standardized ways.

docs/howtolens/chapter_3_pixelizations.md

@@ -0,0 +1,41 @@
+# Chapter 3: Pixelizations
+
+In chapter 3, we use **Pixelizations** to reconstruct complex source galaxies on pixelized grids.
+
+The chapter contains the following tutorials:
+
+[Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/notebooks/chapter_3_pixelizations/tutorial_12_adaptive_regularization.ipynb)
+\- Adapting the regularization to the source's morphology.

docs/howtolens/chapter_3_search_chaining.md

@@ -0,0 +1,24 @@
+# Chapter 4: Scaling Up Lensing
+
+In chapter 4, we scale lens modeling up beyond a single lens galaxy: extra galaxies, multi-galaxy lenses,
+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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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/2026.8.4.1/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_4_pixelizations.md

@@ -6,16 +6,18 @@ The best way to learn **PyAutoLens** is by going through the **HowToLens** lectu
 repository at [PyAutoLabs/HowToLens](https://github.com/PyAutoLabs/HowToLens).
 
 The lectures are provided as Jupyter notebooks (and Python scripts), and they are linked to via this readthedocs. The
-lectures are composed of five chapters
+lectures are composed of four chapters
 
 - **Introduction** - An introduction to strong gravitational lensing and **PyAutolens**.
-- **Lens Modeling** - How to model strong lenses, including a primer on Bayesian non-linear analysis.
-- **Search Chaining** - How to fit complex lens models using non-linear search chaining.
+- **Lens Modeling** - How to model strong lenses, including a primer on Bayesian non-linear analysis and
+  non-linear search chaining.
 - **Pixelizations** - How to perform pixelized reconstructions of the source-galaxy.
+- **Scaling Up Lensing** - Extra galaxies, multi-galaxy lenses, scaling relations, group and cluster
+  scales, and weak lensing.
 
 ## How to Tackle HowToLens
 
-The HowToLens lecture series currently sits at 5 chapters, and each will take around 3-6 hours to go through thoroughly.
+The HowToLens lecture series currently sits at 4 chapters, and each will take around 3-6 hours to go through thoroughly.
 You probably want to be modeling lenses faster than that! Furthermore, the concepts in the
 later chapters are pretty challenging, and familiarity and lens modeling is desirable before you
 tackle them.
@@ -24,7 +26,7 @@ Therefore, we recommend that you complete chapters 1 & 2 and then apply what you
 and real strong lenses imaging, using the scripts found in the
 [autolens_workspace](https://github.com/PyAutoLabs/autolens_workspace) `modeling` packages. Once you're
 confident with your use of **PyAutoLens**, you can then begin to cover the advanced functionality covered in chapters
-3, 4 & 5.
+3 & 4.
 
 ## Lensing Theory
 

docs/howtolens/chapter_4_scaling_up_lensing.md

@@ -249,8 +249,8 @@ general/credits
 howtolens/howtolens
 howtolens/chapter_1_introduction
 howtolens/chapter_2_lens_modeling
-howtolens/chapter_3_search_chaining
-howtolens/chapter_4_pixelizations
+howtolens/chapter_3_pixelizations
+howtolens/chapter_4_scaling_up_lensing
 howtolens/chapter_optional
 ```