autolens 0.23.5

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:

PyAutoLens is based on the following papers:

Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging

AutoLens: Automated Modeling of a Strong Lens's Light, Mass & Source

Python Example

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.

import autofit as af
from autolens.pipeline.phase import phase_imaging, phase_hyper
from autolens.data.array import mask as msk
from autolens.model.galaxy import galaxy_model as gm
from autolens.data import ccd
from autolens.model.profiles import light_profiles as lp
from autolens.model.profiles import mass_profiles as mp
from autolens.lens.plotters import lens_fit_plotters

import os

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

# Get the relative path to the data in our workspace & load the ccd imaging data.
path = '{}/../'.format(os.path.dirname(os.path.realpath(__file__)))

lens_name = 'example_lens'

ccd_data = ccd.load_ccd_data_from_fits(
    image_path=path + '/data/' + lens_name + '/image.fits',
    psf_path=path+'/data/'+lens_name+'/psf.fits',
    noise_map_path=path+'/data/'+lens_name+'/noise_map.fits', 
    pixel_scale=0.1)

# Create a mask for the data, which we setup as a 3.0" circle.
mask = msk.Mask.circular(
shape=ccd_data.shape, pixel_scale=ccd_data.pixel_scale, radius_arcsec=3.0)

# We model our lens galaxy using a mass profile (a singular isothermal ellipsoid) & our source galaxy 
# a light profile (an elliptical Sersic). We load these profiles from the 'light_profiles (lp)' & 
# 'mass_profiles (mp)' modules.
lens_mass_profile = mp.EllipticalIsothermal
source_light_profile = lp.EllipticalSersic

# To setup our model galaxies, we use the GalaxyModel class, which represents a galaxy whose parameters 
# are variable & fitted for by PyAutoLens. The galaxies are also assigned redshifts.
lens_galaxy_model = gm.GalaxyModel(redshift=0.5, mass=lens_mass_profile)
source_galaxy_model = gm.GalaxyModel(redshsift=1.0, light=source_light_profile)

# To perform the analysis, we set up a phase using the 'phase' module (imported as 'ph').
# A phase takes our galaxy models & fits their parameters using a non-linear search 
# (in this case, MultiNest).
phase = phase_imaging.LensSourcePlanePhase(
    lens_galaxies=dict(lens=lens_galaxy_model),
    source_galaxies=dict(source_galaxy_model),
    phase_name='example/phase_example', optimizer_class=af.MultiNest)

# We pass the ccd data to the phase, thereby fitting it with the lens model above & plot the resulting fit.
result = phase.run(data=ccd_data)
lens_fit_plotters.plot_fit_subplot(fit=result.most_likely_fit)

Slack

We're building a PyAutoLens community on Slack, so you should contact us on our Slack channel 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 requesting an invite.

Features

PyAutoLens's advanced modeling features include:

• Galaxies - Use light & mass profiles to make galaxies & perform lensing calculations (potentials, deflections, etc).
• Pipelines - Write automated analysis pipelines to fit complex lens models to large samples of strong lenses.
• Extended Sources - Reconstruct complex source galaxy morphologies on a variety of pixel-grids.
• Adaption - Adapt the lensing analysis to the features of the observed strong lens imaging.
• Multi-Plane - Perform multi-plane ray-tracing & model multi-plane lens systems.
• Visualization - Custom visualization libraries for plotting physical lensing quantities & modeling results.

HowToLens

Included with PyAutoLens is the HowToLens lecture series, which provides an introduction to strong gravitational lens modeling with PyAutoLens. It can be found in the workspace & consists of 4 chapters:

• Introduction - An introduction to strong gravitational lensing & PyAutolens.
• Lens Modeling - How to model strong lenses, including a primer on Bayesian non-linear analysis.
• Pipelines - How to build pipelines & tailor them to your own science case.
• Inversions - How to perform pixelized reconstructions of the source-galaxy.

Workspace

PyAutoLens comes with a workspace, which can be found here & which includes the following:

• Config - Configuration files which customize the PyAutoLens analysis.
• Data - Your data folder, including example data-sets distributed with PyAutoLens.
• HowToLens - The HowToLens lecture series.
• Output - Where the PyAutoLens analysis & visualization are output.
• Pipelines - Example pipelines for modeling strong lenses or to use a template for your own pipeline.
• Plotting - Scripts enabling customized figures & images.
• Runners - Scripts for running a PyAutoLens pipeline.
• Tools - Tools for simulating strong lens data, creating masks & using many other PyAutoLens features.

If you install PyAutoLens with conda or pip, you will need to download the workspace from the autolens_workspace repository, which is described in the installation instructions below.

Depedencies

PyAutoLens requires PyMultiNest & Numba.

Installation with conda

We recommend installation using a conda environment as this circumvents a number of compatibility issues when installing PyMultiNest.

First, install conda.

Create a conda environment:

conda create -n autolens python=3.7 anaconda

Install multinest:

conda install -c conda-forge multinest

Tell matplotlib what backend to use:

echo "backend : TKAgg" > ~/.matplotlib/matplotlibrc

Activate the conda environment:

conda activate autolens

Install autolens:

pip install autolens

Clone autolens workspace & set WORKSPACE enviroment variable:

cd /path/where/you/want/autolens_workspace
git clone https://github.com/Jammy2211/autolens_workspace
export WORKSPACE=/path/to/autolens_workspace/

Set PYTHONPATH to include the autolens_workspace directory:

export PYTHONPATH=/path/to/autolens_workspace/

You can test everything is working by running the example pipeline runner in the autolens_workspace

python3 /path/to/autolens_workspace/runners/simple/runner_lens_mass_&_source.py

Installation with pip

Installation is also available via pip, however there are reported issues with installing PyMultiNest that can make installation difficult, see the file INSTALL.notes

$ pip install autolens

Clone autolens workspace & set WORKSPACE enviroment variable:

cd /path/where/you/want/autolens_workspace
git clone https://github.com/Jammy2211/autolens_workspace
export WORKSPACE=/path/to/autolens_workspace/

Set PYTHONPATH to include the autolens_workspace directory:

export PYTHONPATH=/path/to/autolens_workspace/

You can test everything is working by running the example pipeline runner in the autolens_workspace

python3 /path/to/autolens_workspace/runners/simple/runner_lens_mass_&_source.py

Support & Discussion

If you're having difficulty with installation, lens modeling, or just want a chat, feel free to message us on our Slack channel.

Contributing

If you have any suggestions or would like to contribute please get in touch.

Publications

The following papers use PyAutoLens:

The molecular-gas properties in the gravitationally lensed merger HATLAS J142935.3-002836

Galaxy structure with strong gravitational lensing: decomposing the internal mass distribution of massive elliptical galaxies

Novel Substructure & Superfluid Dark Matter

CO, H2O, H2O+ line & dust emission in a z = 3.63 strongly lensed starburst merger at sub-kiloparsec scales

Credits

James Nightingale - Lead developer & PyAutoLens guru.

Richard Hayes - Lead developer & PyAutoFit guru.

Ashley Kelly - Developer of pyquad for fast deflections computations.

Nan Li - Docker integration & support.

Andrew Robertson - Critical curve & caustic calculations.

Andrea Enia - Voronoi source-plane plotting tools.