Winter 2022 Release

The primary new functionality are new source-plane pixelization (Delaunay triangulations and a Voronoi mesh) and regularization schemes which:

• Use interpolation when pairing source-pixels to traced image-pixels.
• Use a derivate evaluation scheme to derive the regularization.

These offer a general improvement to the quality of lens modeling using inversions and they correspond to the following classes:

https://pyautolens.readthedocs.io/en/latest/api/generated/autoarray.inversion.pixelizations.DelaunayMagnification.html#autoarray.inversion.pixelizations.DelaunayMagnification

https://pyautolens.readthedocs.io/en/latest/api/generated/autoarray.inversion.pixelizations.DelaunayBrightnessImage.html#autoarray.inversion.pixelizations.DelaunayBrightnessImage

https://pyautolens.readthedocs.io/en/latest/api/generated/autoarray.inversion.pixelizations.VoronoiNNMagnification.html#autoarray.inversion.pixelizations.VoronoiNNMagnification

https://pyautolens.readthedocs.io/en/latest/api/generated/autoarray.inversion.pixelizations.VoronoiNNBrightnessImage.html#autoarray.inversion.pixelizations.VoronoiNNBrightnessImage

Other features include:

• Directly fitting a lens model to a lens model quantity (e.g. the deflection angles, convergence) as opposed to using data (https://github.com/Jammy2211/autolens_workspace/tree/release/scripts/misc/model_quantity).

• Cored steep elliptical (CSE) implementation of various stellar and dark matter mass profiles for deflection calculation speed up (https://arxiv.org/pdf/2106.11464.pdf).

• Simulating lens datasets where the source signal-to-noise ratio is an input (https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/imaging/simulators/misc/manual_signal_to_noise_ratio.ipynb).