Features#
This page provides an overview of the advanced features of PyAutoLens.
Firstly, brief one sentence descriptions of each feature are given, with more detailed descriptions below including links to the relevant workspace examples.
Pixelizations: Reconstructing the source galaxy on a mesh of pixels, to capture extremely irregular structures like spiral arms.
Point Sources: Modeling point sources (e.g. quasars) observed in the strong lens imaging data.
Interferometry: Modeling of interferometer data (e.g. ALMA, LOFAR) directly in the uv-plane.
Multi Gaussian Expansion (MGE): Decomposing the lens galaxy into hundreds of Gaussians, for a clean lens subtraction.
Groups: Modeling group-scale strong lenses with multiple lens galaxies and multiple source galaxies.
Multi-Wavelength: Simultaneous analysis of imaging and / or interferometer datasets observed at different wavelengths.
Ellipse Fitting: Fitting ellipses to determine a lens galaxy’s ellipticity, position angle and centre.
Shapelets: Decomposing a galaxy into a set of shapelet orthogonal basis functions, capturing more complex structures than simple light profiles.
Operated Light Profiles: Assuming a light profile has already been convolved with the PSF, for when the PSF is a significant effect.
Sky Background: Including the background sky in the model to ensure robust fits to the outskirts of galaxies.
Pixelizations#
Pixelizations reconstruct the source galaxy’s light on a pixel-grid. Unlike LightProfile
’s, they are able to
reconstruct the light of non-symmetric, irregular and clumpy sources.
The image below shows a pixelized source reconstruction of the strong lens SLACS1430+4105, where the source is reconstructed on a Voronoi mesh adapted to the source morphology, revealing it to be a grand-design face on spiral galaxy:
A complete overview of pixelized source reconstructions can be found
at notebooks/overview/overview_5_pixelizations.ipynb
.
Chapter 4 of the HowToLens lectures describes pixelizations in detail and teaches users how they can be used to perform lens modeling.
Point Sources#
There are many lenses where the background source is not extended but is instead a point-source, for example strongly lensed quasars and supernovae.
For these objects, we do not want to model the source using a light profile, which implicitly assumes an extended surface brightness distribution.
Instead, we assume that our source is a point source with a centre (y,x), and ray-trace triangles at iteratively higher resolutions to determine the source’s exact locations in the image-plane:
Note that the image positions above include the fifth central image of the strong lens, which is often not seen in strong lens imaging data. It is easy to disable this image in the point source modeling.
Checkout the autolens_workspace/*/point_source
package to get started.
Interferometry#
Modeling of interferometer data from submillimeter (e.g. ALMA) and radio (e.g. LOFAR) observatories:
Visibilities data is fitted directly in the uv-plane, circumventing issues that arise when fitting a dirty image such as correlated noise. This uses the non-uniform fast fourier transform algorithm [PyNUFFT](https://github.com/jyhmiinlin/pynufft) to efficiently map the galaxy model images to the uv-plane.
Checkout the autolens_workspace/*/interferometer
package to get started.
Multi Gaussian Expansion (MGE)#
An MGE decomposes the light of a galaxy into tens or hundreds of two dimensional Gaussians:
In the image above, 30 Gaussians are shown, where their sizes go from below the pixel scale (in order to resolve point emission) to beyond the size of the galaxy (to capture its extended emission).
An MGE is an extremely powerful way to model and subtract the light of the foreground lens galaxy in strong lens imaging, and makes it possible to model the stellar mass of the lens galaxy in a way that is tied to its light.
Scientific Applications include capturing departures from elliptical symmetry in the light of galaxies, providing a flexible model to deblend the emission of point sources (e.g. quasars) from the emission of their host galaxy and deprojecting the light of a galaxy from 2D to 3D.
The following paper gives a detailed overview of MGEs and their applications in strong lensing: https://arxiv.org/abs/2403.16253
Checkout autolens_workspace/notebooks/features/multi_gaussian_expansion.ipynb
to learn how to use an MGE.
Groups#
The strong lenses we’ve discussed so far have just a single lens galaxy responsible for the lensing. Group-scale strong lenses are systems where there two or more lens galaxies deflecting one or more background sources:
PyAutoLens has built in tools for modeling group-scale lenses, with no limit on the number of lens and source galaxies!
Overviews of group and analysis are given in notebooks/overview/overview_9_groups.ipynb
The autolens_workspace/*/group
package has example scripts for simulating datasets and lens modeling.
Multi-Wavelength#
Modeling imaging datasets observed at different wavelengths (e.g. HST F814W and F150W) simultaneously or simultaneously analysing imaging and interferometer data:
The appearance of the strong changes as a function of wavelength, therefore multi-wavelength analysis means we can learn more about the different components in a galaxy (e.g a redder bulge and bluer disk) or when imaging and interferometer data are combined, we can compare the emission from stars and dust.
Checkout the autolens_workspace/*/multi
package to get started, however combining datasets is a more advanced
feature and it is recommended you first get to grips with the core API.
Ellipse Fitting#
Ellipse fitting is a technique which fits many ellipses to a galaxy’s emission to determine its ellipticity, position angle and centre, without assuming a parametric form for its light (e.g. like a Seisc profile):
This provides complementary information to parametric light profile fitting, for example giving insights on whether the ellipticity and position angle are constant with radius or if the galaxy’s emission is lopsided.
There are also multipole moment extensions to ellipse fitting, which determine higher order deviations from elliptical symmetry providing even more information on the galaxy’s structure.
The following paper describes the technique in detail: https://arxiv.org/html/2407.12983v1
Checkout autolens_workspace/notebooks/features/ellipse_fitting.ipynb
to learn how to use ellipse fitting.
Shapelets#
Shapelets are a set of orthogonal basis functions that can be combined the represent galaxy structures:
Scientific Applications include capturing symmetric structures in a galaxy which are more complex than a Sersic profile, irregular and asymmetric structures in a galaxy like spiral arms and providing a flexible model to deblend the emission of point sources (e.g. quasars) from the emission of their host galaxy.
Checkout autolens_workspace/notebooks/features/shapelets.ipynb
to learn how to use shapelets.
Operated Light Profiles#
An operated light profile is one where it is assumed to already be convolved with the PSF of the data, with the
Moffat
and Gaussian
profiles common choices:
They are used for certain scientific applications where the PSF convolution is known to be a significant effect and the knowledge of the PSF allows for detailed modeling abd deblending of the galaxy’s light.
Checkout autogalaxy_workspace/notebooks/features/operated_light_profiles.ipynb
to learn how to use operated profiles.
Sky Background#
When an image of a galaxy is observed, the background sky contributes light to the image and adds noise:
For detailed studies of the outskirts of galaxies (e.g. stellar halos, faint extended disks), the sky background must be accounted for in the model to ensure robust and accurate fits.
Checkout autogalaxy_workspace/notebooks/features/sky_background.ipynb
to learn how to use include the sky
background in your model.
Other#
mass models (aris paper)
Automated pipelines / SLaM.
Dark matter subhalos.
Graphical models.