Chapter 2: Lens Modeling

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:

Tutorial 1: Non-linear Search - How a non-linear search is used to fit a lens model and the concepts of a parameter space and priors.

Tutorial 2: Practicalities - Practicalities of performing model-fitting, like how to inspect the results on your hard-disk.

Tutorial 3: Realism and Complexity - Finding a balance between realism and complexity when composing and fitting a lens model.

Tutorial 4: Dealing with Failure - What to do when PyAutoLens finds an inaccurate lens model.

Tutorial 5: Linear Profiles - Light profiles which capture complex morphologies in a reduced number of non-linear parameters.

Tutorial 6: Masking and Positions - How to mask and mark positions on your data to improve the lens model.

Tutorial 7: Results - Overview of the results available after successfully fitting a lens model.

Tutorial 8: Need for Speed - How to fit complex models whilst balancing efficiency and run-time.

Tutorial 9: Search Chaining - Breaking the lens modeling procedure into a chained sequence of model-fits.

Tutorial 10: Prior Passing - How the results of earlier searches are passed to later searches.

Tutorial 11: SLaM - Template pipelines for fitting lens models in standardized ways.