PyAutoLens Likelihood Function

The autolens_workspace contains Jupyter notebooks describing the (log) likelihood functions used by PyAutoLens.

The notebooks provide a step-by-step guide of how PyAutoLens fits strong lens data, with the aim to make the analysis clear to readers without background experience in strong lens modeling and make the modeling less of a "black box".

We recommend that when writing a paper using PyAutoLens the author links to this GitHub repository when describing their likelihood function.

The notebooks are not stored here (they are on the autolens_workspace), however URLs to every notebook are provided here. We recommend authors link to this GitHub repository (as opposed to direct links to each) because the URLs to notebooks on the autolens_workspace may change after papers are published.

By linking to this repository a permanent URL is provided.

Notebook Links

There are different ways a strong lens can be modeled in PyAutoLens, we provide links describing the likelihood function of all approaches below:

Imaging Dataset + Parametric Source:

https://github.com/Jammy2211/autolens_workspace/blob/main/notebooks/imaging/advanced/log_likelihood_function/parametric.ipynb

Imaging Dataset + Pixelized Source Reconstruction:

https://github.com/Jammy2211/autolens_workspace/blob/main/notebooks/imaging/advanced/log_likelihood_function/pixelization.ipynb