Open-Source Strong Lensing
PyAutoLens: Open-Source Strong Lensing
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Installation Guide <https://pyautolens.readthedocs.io/en/latest/installation/overview.html>_ |
readthedocs <https://pyautolens.readthedocs.io/en/latest/index.html>_ |
Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/master?filepath=introduction.ipynb>_ |
When two or more galaxies are aligned perfectly down our line-of-sight, the background galaxy appears multiple times. This is called strong gravitational lensing and PyAutoLens makes it simple to model strong gravitational lenses, like this one:
The following links are useful for new starters:
The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/master?filepath=introduction.ipynb>_, where you can try PyAutoLens in a web browser (without installation).
The PyAutoLens readthedocs <https://pyautolens.readthedocs.io/en/latest>, which includes
an installation guide <https://pyautolens.readthedocs.io/en/latest/installation/overview.html>and an overview of PyAutoLens's core features.
The autolens_workspace GitHub repository <https://github.com/Jammy2211/autolens_workspace>, which includes example scripts and the
HowToLens Jupyter notebook tutorials <https://github.com/Jammy2211/autolens_workspace/tree/master/notebooks/howtolens>which give new users a step-by-step introduction to PyAutoLens.
Lensing calculations are performed in PyAutoLens by building a
Tracer object from
Galaxy objects. Below, we create a simple strong lens system where a redshift 0.5
Galaxy with an
MassProfile lenses a background source at redshift 1.0 with an
LightProfile representing a disk.
.. code-block:: python
import autolens as al import autolens.plot as aplt from astropy import cosmology as cosmo """ To describe the deflection of light by mass, two-dimensional grids of (y,x) Cartesian coordinates are used. """ grid = al.Grid2D.uniform( shape_native=(50, 50), pixel_scales=0.05, # <- Conversion from pixel units to arc-seconds. ) """ The lens galaxy has an elliptical isothermal mass profile and is at redshift 0.5. """ mass = al.mp.EllIsothermal( centre=(0.0, 0.0), elliptical_comps=(0.1, 0.05), einstein_radius=1.6 ) lens_galaxy = al.Galaxy(redshift=0.5, mass=mass) """ The source galaxy has an elliptical exponential light profile and is at redshift 1.0. """ disk = al.lp.EllExponential( centre=(0.3, 0.2), elliptical_comps=(0.05, 0.25), intensity=0.05, effective_radius=0.5, ) source_galaxy = al.Galaxy(redshift=1.0, disk=disk) """ We create the strong lens using a Tracer, which uses the galaxies, their redshifts and an input cosmology to determine how light is deflected on its path to Earth. """ tracer = al.Tracer.from_galaxies( galaxies=[lens_galaxy, source_galaxy], cosmology=cosmo.Planck15 ) """ We can use the Grid2D and Tracer to perform many lensing calculations, for example plotting the image of the lensed source. """ tracer_plotter = aplt.TracerPlotter(tracer=tracer, grid=grid) tracer_plotter.figures_2d(image=True)
With PyAutoLens, you can begin modeling a lens in just a couple of minutes. The example below demonstrates
a simple analysis which fits the lens galaxy's mass with an
EllIsothermal and the source galaxy's light
.. code-block:: python
import autofit as af import autolens as al import autolens.plot as aplt """ Load Imaging data of the strong lens from the dataset folder of the workspace. """ imaging = al.Imaging.from_fits( image_path="/path/to/dataset/image.fits", noise_map_path="/path/to/dataset/noise_map.fits", psf_path="/path/to/dataset/psf.fits", pixel_scales=0.1, ) """ Create a mask for the imaging data, which we setup as a 3.0" circle, and apply it. """ mask = al.Mask2D.circular( shape_native=imaging.shape_native, pixel_scales=imaging.pixel_scales, radius=3.0 ) imaging = imaging.apply_mask(mask=mask) """ We model the lens galaxy using an elliptical isothermal mass profile and the source galaxy using an elliptical sersic light profile. """ lens_mass_profile = al.mp.EllIsothermal source_light_profile = al.lp.EllSersic """ To setup these profiles as model components whose parameters are free & fitted for we set up each Galaxy as a Model and define the model as a Collection of all galaxies. """ lens_galaxy_model = af.Model(al.Galaxy, redshift=0.5, mass=lens_mass_profile) source_galaxy_model = af.Model(al.Galaxy, redshift=1.0, disk=source_light_profile) model = af.Collection(lens=lens_galaxy_model, source=source_galaxy_model) """ We define the non-linear search used to fit the model to the data (in this case, Dynesty). """ search = af.DynestyStatic(name="search[example]", nlive=50) """ We next set up the `Analysis`, which contains the `log likelihood function` that the non-linear search calls to fit the lens model to the data. """ analysis = al.AnalysisImaging(dataset=imaging) """ To perform the model-fit we pass the model and analysis to the search's fit method. This will output results (e.g., dynesty samples, model parameters, visualization) to hard-disk. """ result = search.fit(model=model, analysis=analysis) """ The results contain information on the fit, for example the maximum likelihood model from the Dynesty parameter space search. """ print(result.samples.max_log_likelihood_instance)
Support for installation issues, help with lens modeling and using PyAutoLens is available by
raising an issue on the GitHub issues page <https://github.com/Jammy2211/PyAutoLens/issues>_.
We also offer support on the PyAutoLens
Slack channel <https://pyautolens.slack.com/>, where we also provide the
latest updates on PyAutoLens. Slack is invitation-only, so if you'd like to join send
email <https://github.com/Jammy2211> requesting an invite.
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