Installation Guide | readthedocs | Introduction on Binder | HowToLens
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.
The following links are useful for new starters:
- The PyAutoLens readthedocs: which includes an overview of PyAutoLens's core features, a new user starting guide and an installation guide.
- The introduction Jupyter Notebook on Binder: try PyAutoLens in a web browser (without installation).
- The autolens_workspace GitHub repository: example scripts and the HowToLens Jupyter notebook lectures.
Support for installation issues, help with lens modeling and using PyAutoLens is available by raising an issue on the GitHub issues page.
We also offer support on the PyAutoLens Slack channel, where we also provide the latest updates on PyAutoLens. Slack is invitation-only, so if you'd like to join send an email requesting an invite.
For users less familiar with gravitational lensing, Bayesian inference and scientific analysis you may wish to read through the HowToLens lectures. These teach you the basic principles of gravitational lensing and Bayesian inference, with the content pitched at undergraduate level and above.
A complete overview of the lectures is provided on the HowToLens readthedocs page
Lensing calculations are performed in PyAutoLens by building a Tracer object from LightProfile,
MassProfile and Galaxy objects. Below, we create a simple strong lens system where a redshift 0.5
lens Galaxy with an Isothermal MassProfile lenses a background source at redshift 1.0 with an
Exponential LightProfile representing a disk.
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.Isothermal(
centre=(0.0, 0.0), ell_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.Exponential(
centre=(0.3, 0.2),
ell_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(
galaxies=[lens_galaxy, source_galaxy],
cosmology = al.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 minutes. The example below demonstrates a simple analysis which
fits the lens galaxy's mass with an Isothermal and the source galaxy's light with a Sersic.
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.
"""
dataset = al.Imaging.from_fits(
data_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=dataset.shape_native,
pixel_scales=dataset.pixel_scales,
radius=3.0
)
dataset = dataset.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.
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:
mass = af.Model(al.mp.Isothermal)
lens = af.Model(al.Galaxy, redshift=0.5, mass=lens_mass_profile)
# Source:
disk = af.Model(al.lp.Sersic)
source = af.Model(al.Galaxy, redshift=1.0, disk=disk)
# Overall Lens Model:
model = af.Collection(galaxies=af.Collection(lens=lens, source=source))
"""
We define the non-linear search used to fit the model to the data (in this case, Dynesty).
"""
search = af.Nautilus(name="search[example]", n_live=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=dataset)
"""
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())