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notebooks/chapter_1_introduction/README.md

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- [Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb) — Setting up **PyAutoGalaxy**'s visualization library.
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- [Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb) — Setting up **PyAutoGalaxy**'s visualization library.
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- [Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb) — How grids of (y,x) coordinates are used to create images of galaxies that ultimately quantify their morphology.
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- [Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb) — How grids of (y,x) coordinates are used to create images of galaxies that ultimately quantify their morphology.
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- [Tutorial 2: Data](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_2_data.ipynb) — Simulating and inspecting telescope imaging data of a galaxy, for example from the Hubble Space Telescope.
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- [Tutorial 2: Data](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_2_data.ipynb) — Simulating and inspecting telescope imaging data of a galaxy, for example from the Hubble Space Telescope.
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- [Tutorial 3: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb) — How to fit imaging data of a galaxy and quantify whether a fit is good or bad.
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- [Tutorial 3: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb) — How to fit imaging data of a galaxy and quantify whether a fit is good or bad.
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- [Tutorial 4: Methods](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb) — An overview of the different methods used to fit galaxies with.
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- [Tutorial 4: Methods](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb) — An overview of the different methods used to fit galaxies with.
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- [Tutorial 5: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb) — A summary of the chapter.
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- [Tutorial 5: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb) — A summary of the chapter.

notebooks/chapter_2_modeling/README.md

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- [Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/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.
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- [Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/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.
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- [Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_2_practicalities.ipynb) — Practicalities of performing model-fitting, like how to inspect the results on your hard-disk.
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- [Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_2_practicalities.ipynb) — Practicalities of performing model-fitting, like how to inspect the results on your hard-disk.
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- [Tutorial 3: Realism and Complexity](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb) — Finding a balance between realism and complexity when composing and fitting a model.
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- [Tutorial 3: Realism and Complexity](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb) — Finding a balance between realism and complexity when composing and fitting a model.
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- [Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb) — What to do when PyAutoGalaxy finds an inaccurate model.
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- [Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb) — What to do when PyAutoGalaxy finds an inaccurate model.
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- [Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb) — Light profiles which capture complex morphologies in a reduced number of non-linear parameters.
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- [Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb) — Light profiles which capture complex morphologies in a reduced number of non-linear parameters.
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- [Tutorial 6: Masking](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb) — How to mask your data to improve the model.
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- [Tutorial 6: Masking](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb) — How to mask your data to improve the model.
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- [Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_7_results.ipynb) — Overview of the results available after successfully fitting a model.
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- [Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_7_results.ipynb) — Overview of the results available after successfully fitting a model.
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- [Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb) — How to fit complex models whilst balancing efficiency and run-time.
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- [Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb) — How to fit complex models whilst balancing efficiency and run-time.

notebooks/chapter_3_search_chaining/README.md

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- [Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb) — Breaking the modeling procedure into a chained sequence of model-fits.
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- [Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb) — Breaking the modeling procedure into a chained sequence of model-fits.
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- [Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb) — How the results of earlier searches are passed to later searches.
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- [Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb) — How the results of earlier searches are passed to later searches.
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- [Tutorial 3: x2 Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb) — Modeling two galaxies simultaneously using chained searches.
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- [Tutorial 3: x2 Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb) — Modeling two galaxies simultaneously using chained searches.

notebooks/chapter_4_pixelizations/README.md

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- [Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb) — Creating a pixel-grid in the source-plane.
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- [Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb) — Creating a pixel-grid in the source-plane.
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- [Tutorial 2: Mappers](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb) — How a pixelization maps source-pixels to image-pixels.
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- [Tutorial 2: Mappers](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb) — How a pixelization maps source-pixels to image-pixels.
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- [Tutorial 3: Inversions](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb) — Inverting the mappings to reconstruct the source's light.
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- [Tutorial 3: Inversions](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb) — Inverting the mappings to reconstruct the source's light.
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- [Tutorial 4: Bayesian Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb) — Smoothing the source within a Bayesian framework.
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- [Tutorial 4: Bayesian Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb) — Smoothing the source within a Bayesian framework.
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- [Tutorial 5: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_4_pixelizations/tutorial_5_model_fit.ipynb) — An example modeling pipeline which uses an inversion.
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- [Tutorial 5: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_4_pixelizations/tutorial_5_model_fit.ipynb) — An example modeling pipeline which uses an inversion.

scripts/chapter_1_introduction/README.md

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# Files
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- [Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb) — Setting up **PyAutoGalaxy**'s visualization library.
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- [Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb) — Setting up **PyAutoGalaxy**'s visualization library.
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- [Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb) — How grids of (y,x) coordinates are used to create images of galaxies that ultimately quantify their morphology.
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- [Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb) — How grids of (y,x) coordinates are used to create images of galaxies that ultimately quantify their morphology.
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- [Tutorial 2: Data](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_2_data.ipynb) — Simulating and inspecting telescope imaging data of a galaxy, for example from the Hubble Space Telescope.
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- [Tutorial 2: Data](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_2_data.ipynb) — Simulating and inspecting telescope imaging data of a galaxy, for example from the Hubble Space Telescope.
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- [Tutorial 3: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb) — How to fit imaging data of a galaxy and quantify whether a fit is good or bad.
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- [Tutorial 3: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb) — How to fit imaging data of a galaxy and quantify whether a fit is good or bad.
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- [Tutorial 4: Methods](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb) — An overview of the different methods used to fit galaxies with.
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- [Tutorial 4: Methods](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb) — An overview of the different methods used to fit galaxies with.
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- [Tutorial 5: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb) — A summary of the chapter.
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- [Tutorial 5: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb) — A summary of the chapter.

scripts/chapter_2_modeling/README.md

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- [Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/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.
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- [Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/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.
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- [Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_2_practicalities.ipynb) — Practicalities of performing model-fitting, like how to inspect the results on your hard-disk.
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- [Tutorial 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_2_practicalities.ipynb) — Practicalities of performing model-fitting, like how to inspect the results on your hard-disk.
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- [Tutorial 3: Realism and Complexity](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb) — Finding a balance between realism and complexity when composing and fitting a model.
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- [Tutorial 3: Realism and Complexity](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb) — Finding a balance between realism and complexity when composing and fitting a model.
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- [Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb) — What to do when PyAutoGalaxy finds an inaccurate model.
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- [Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb) — What to do when PyAutoGalaxy finds an inaccurate model.
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- [Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb) — Light profiles which capture complex morphologies in a reduced number of non-linear parameters.
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- [Tutorial 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb) — Light profiles which capture complex morphologies in a reduced number of non-linear parameters.
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- [Tutorial 6: Masking](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb) — How to mask your data to improve the model.
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- [Tutorial 6: Masking](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb) — How to mask your data to improve the model.
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- [Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_7_results.ipynb) — Overview of the results available after successfully fitting a model.
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- [Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_7_results.ipynb) — Overview of the results available after successfully fitting a model.
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- [Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb) — How to fit complex models whilst balancing efficiency and run-time.
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- [Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb) — How to fit complex models whilst balancing efficiency and run-time.

scripts/chapter_3_search_chaining/README.md

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- [Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb) — Breaking the modeling procedure into a chained sequence of model-fits.
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- [Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb) — Breaking the modeling procedure into a chained sequence of model-fits.
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- [Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb) — How the results of earlier searches are passed to later searches.
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- [Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb) — How the results of earlier searches are passed to later searches.
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- [Tutorial 3: x2 Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.9.1/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb) — Modeling two galaxies simultaneously using chained searches.
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- [Tutorial 3: x2 Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.7.15.1/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb) — Modeling two galaxies simultaneously using chained searches.

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