PyAutoLens can reconstruct the light of a strongly lensed source-galaxy using a pixel-grid, using a process
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:
To fit this image with an
Inversion, we first mask the
mask = al.Mask2D.circular( shape_native=imaging.shape_native, pixel_scales=imaging.pixel_scales, radius=3.6 ) imaging = imaging.apply_mask(mask=mask_2d)
To reconstruct the source on a pixel-grid, called a mesh, we simply pass it the
Mesh class we want to reconstruct its
We also pass a
Regularization scheme which applies a smoothness prior on the source reconstruction.
Below, we use a
Rectangular mesh with resolution 40 x 40 and a
Constant regularization scheme:
pixelization = al.Pixelization( mesh=al.mesh.Rectangular(shape=(40, 40)), regularization=al.reg.Constant(coefficient=1.0), ) source_galaxy = al.Galaxy( redshift=1.0, pixelization=pixelization )
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
Regularization to reconstruct
the lensed source’s light using the
tracer = al.Tracer.from_galaxies(galaxies=[lens_galaxy, source_galaxy]) fit = al.FitImaging(dataset=imaging, tracer=tracer)
Here is what our reconstructed source galaxy looks like:
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:
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.
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:
This was a brief overview of
Inverion’s with PyAutoLens.
There is a lot more to using
Inverion’s 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.
Regularizationschemes that adapt to the source galaxy being reconstructed.