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Open-Source Strong Lensing

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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>_ | HowToLens <https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html>_

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:

.. image:: https://github.com/Jammy2211/PyAutoLens/blob/master/files/imageaxis.png

Getting Started

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.

API Overview

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 EllIsothermal MassProfile lenses a background source at redshift 1.0 with an EllExponential 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 with an EllSersic.

.. 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

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 an email <https://github.com/Jammy2211> requesting an invite.

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