Pixelized Sources

PyAutoLens can reconstruct the light of a strongly lensed source-galaxy using a pixel-grid, using a process called an LinearEqn.

Lets use a Pixelization to reconstruct the source-galaxy of the image below, noting how complex the lensed source appears, with multiple rings and clumps of light:

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Rectangular Example

To fit this image with an LinearEqn, we first mask the Imaging object:

mask = al.Mask2D.circular(
   shape_native=imaging.shape_native, pixel_scales=imaging.pixel_scales, radius=3.6
 )

imaging = imaging.apply_mask(mask=mask)

To reconstruct the source using a pixel-grid, we simply pass it the Pixelization class we want to reconstruct its light using. We also pass a Regularization scheme which applies a smoothness prior on the source reconstruction.

Below, we use a Rectangular pixelization with resolution 40 x 40 and a Constant regularization scheme:

source_galaxy = al.Galaxy(
    redshift=1.0,
    pixelization=al.pix.Rectangular(shape=(40, 40)),
    regularization=al.reg.Constant(coefficient=1.0),
)

To fit the data, we simply pass this source-galaxy into a Tracer (complete with lens galaxy mass model). The FitImaging object will automatically use the source galaxy’s Pixelization and Regularization to reconstruct the lensed source’s light using the LinearEqn:

tracer = al.Tracer.from_galaxies(galaxies=[lens_galaxy, source_galaxy])

fit = al.FitImaging(imaging=imaging, tracer=tracer)

Here is what our reconstructed source galaxy looks like:

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Note how the source reconstruction is irregular and has multiple clumps of light, these features would be difficult to represent using analytic light profiles!

The source reconstruction can be mapped back to the image-plane, to produce a reconstructed image:

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Voronoi Example

PyAutoLens supports many different pixel-grids. Below, we use a VoronoiMagnification pixelization, which defines the source-pixel centres in the image-plane and ray traces them to the source-plane.

The source pixel-grid is therefore adapted to the mass-model magnification pattern, placing more source-pixel in the highly magnified regions of the source-plane.

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By inspecting the residual-map, normalized residual-map and chi-squared-map of the FitImaging object, we can see how the source reconstruction accurately fits the image of the strong lens:

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Wrap-Up

This was a brief overview of LinearEqns with PyAutoLens. There is a lot more to using LinearEqns then presented here, which is covered in chapters 4 and 5 of the HowToLens, specifically:

  • How the source reconstruction calculates the flux-values of the source pixels when it performs the reconsturction.
  • What exactly regularization is and why it is necessary.
  • The Bayesian framework employed to choose an appropriate level of smoothing and avoid overfitting noise.
  • How to perform lens modeling with inversions.
  • Advanced Pixelization and Regularization schemes that adapt to the source galaxy being reconstructed.