autolens 1.0.16

Automated Strong Gravitational Lens Modeling

PyAutoLens

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, & PyAutoLens makes it simple to model strong gravitational lenses, like this one:

.. image:: https://raw.githubusercontent.com/Jammy2211/PyAutoLens/master/gitimage.png :width: 400 :alt: Alternative text

PyAutoLens is based on the following papers:

Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging <https://arxiv.org/abs/1412.7436>_

AutoLens: Automated Modeling of a Strong Lens's Light, Mass & Source <https://arxiv.org/abs/1708.07377>_

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 Isothermal mass profile lenses a background source at redshift 1.0 with an Exponential light profile.

.. code-block:: python

import autolens as al
import autolens.plot as aplt

"""
To describe the deflection of light grids are used which are two-dimensional Cartesian grids
of (y,x) coordinates which are deflected by mass profiles.
"""

grid = al.Grid.uniform(
    shape_2d=(50, 50),
    pixel_scales=0.05,  # <- Conversion from pixel units to arc-seconds.
)

"""The lens galaxy is at redshift 0.5 and its mass profile is an elliptical Isothermal."""

sie = al.mp.EllipticalIsothermal(
    centre=(0.0, 0.0), elliptical_comps=(0.1, 0.05), einstein_radius=1.6
)

lens_galaxy = al.Galaxy(redshift=0.5, mass=sie)

"""The source galaxy is at redshift 1.0, and its light profile is elliptical Exponential."""

exponential = al.lp.EllipticalExponential(
    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, light=exponential)

"""
We create the strong lens system by performing ray-tracing via a Tracer object, which uses the
galaxies above, 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 tracer to perform many lensing calculations, for example plotting the
image of the lensed source.
"""

aplt.Tracer.image(tracer=tracer, grid=grid)

With PyAutoLens, you can begin modeling a lens in just a couple of minutes. The example below demonstrates a simple analysis which fits the foreground lens galaxy's mass & the background source galaxy's light.

.. code-block:: python

import autofit as af
import autolens as al

import os

"""In this example, we'll fit a simple lens galaxy + source galaxy system."""

dataset_path = "{}/../data".format(os.path.dirname(os.path.realpath(__file__)))
lens_name = "example_lens"

"""Use the relative path to the dataset to load the imaging data."""

imaging = al.Imaging.from_fits(
    image_path=f"{dataset_path}/{lens_name}/image.fits",
    noise_map_path=f"{dataset_path}/{lens_name}/noise_map.fits",
    psf_path=f"{dataset_path}/{lens_name}/psf.fits",
    pixel_scales=0.1,
)

"""Create a mask for the data, which we setup as a 3.0" circle."""

mask = al.Mask.circular(
    shape_2d=imaging.shape_2d, pixel_scales=imaging.pixel_scales, radius=3.0
)

"""
We model our lens galaxy using a mass profile (a singular isothermal ellipsoid) &
our source galaxy a light profile (an elliptical Sersic).
"""

lens_mass_profile = al.mp.EllipticalIsothermal
source_light_profile = al.lp.EllipticalSersic

"""
To setup our model galaxies, we use the GalaxyModel class, which represents a
galaxy whose parameters are free & fitted for by PyAutoLens.
"""

lens_galaxy_model = al.GalaxyModel(redshift=0.5, mass=lens_mass_profile)
source_galaxy_model = al.GalaxyModel(redshift=1.0, light=source_light_profile)

"""
To perform the analysis we set up a phase, which takes our galaxy models & fits
their parameters using a non-linear search (in this case, Dynesty).
"""

phase = al.PhaseImaging(
    galaxies=dict(lens=lens_galaxy_model, source=source_galaxy_model),
    phase_name="example/phase_example",
    search=af.DynestyStatic(n_live_points=50, sampling_efficiency=0.5),
)

"""
We pass the imaging data and mask to the phase, thereby fitting it with the lens
model & plot the resulting fit.
"""

result = phase.run(dataset=imaging, mask=mask)
al.plot.FitImaging.subplot_fit_imaging(fit=result.max_log_likelihood_fit)

Getting Started

To get started checkout our readthedocs <https://pyautolens.readthedocs.io/>_, where you'll find our installation guide, a complete overview of PyAutoLens's features, examples scripts and tutorials and detailed API documentation.

Slack

We're building a PyAutoLens community on Slack, so you should contact us on our Slack channel <https://pyautolens.slack.com/>_ before getting started. Here, I will give you the latest updates on the software & discuss how best to use PyAutoLens for your science case.

Unfortunately, Slack is invitation-only, so first send me an email <https://github.com/Jammy2211>_ requesting an invite.