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Automated Strong Gravitational Lens Modeling

Project description

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>_

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.

.. 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=dataset_path + lens_name + '/image.fits',
    psf_path=dataset_path+lens_name+'/psf.fits',
    noise_map_path=dataset_path+lens_name+'/noise_map.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. The galaxies are also assigned redshifts.
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, MultiNest).
phase = al.PhaseImaging(
    galaxies=dict(lens=lens_galaxy_model,
    source=source_galaxy_model),
    phase_name='example/phase_example',
    non_linear_class=af.MultiNest
    )

# We pass the imaging data and mask to the phase, thereby fitting it with the lens model & plot the resulting fit.
result = phase.run(data=imaging, mask=mask)
al.plot.FitImaging.subplot_fit_imaging(fit=result.max_log_likelihood_fit)

Features

PyAutoLens's advanced modeling features include:

  • Galaxies - Use light & mass profiles to make galaxies & perform lensing calculations.
  • Pipelines - Write automated analysis pipelines to fit complex lens models to large samples of strong lenses.
  • Pixelizations - 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.
  • Interferometry - Model radio / sub-mm interferometer data directly in the uv-plane.
  • Visualization - Custom visualization libraries for plotting physical lensing quantities & modeling results.
  • PyAutoFit - Perform fits using many non-linear searches (MCMC, Nested Sampling) and manipulate large result outputs via the probablistic programming language PyAutoFit <https://github.com/rhayes777/PyAutoFit>_

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 5 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 model-fitting pipelines & tailor them to your own science case.
  • Inversions - How to perform pixelized reconstructions of the source-galaxy.
  • Hyper-Mode - How to use PyAutoLens advanced modeling features that adapt the model to the strong lens being analysed.

Workspace

PyAutoLens comes with a workspace, which can be found here <https://github.com/Jammy2211/autolens_workspace>_ & which includes:

  • Aggregator - Manipulate large suites of modeling results via Jupyter notebooks, using PyAutoFit's in-built results database.
  • API - Illustrative scripts of the PyAutoLens interface, for examples on how to make plots, perform lensing calculations, etc.
  • Config - Configuration files which customize PyAutoLens's behaviour.
  • Dataset - Where data is stored, including example datasets distributed with PyAutoLens.
  • HowToLens - The HowToLens lecture series.
  • Output - Where the PyAutoLens analysis and visualization are output.
  • Pipelines - Example pipelines for modeling strong lenses.
  • Preprocess - Tools to preprocess data before an analysis (e.g. convert units, create masks).
  • Quick Start - A quick start guide, so you can begin modeling lenses within hours.
  • Runners - Scripts for running PyAutoLens pipelines.
  • Simulators - Scripts for simulating strong lens datasets with PyAutoLens.

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.

Documentation & Installation

The PyAutoLens documentation can be found at our readthedocs <https://pyautolens.readthedocs.io/en/master>, including instructions on installation <https://pyautolens.readthedocs.io/en/master/installation.html>.

Contributing

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

Papers

A list of published articles using PyAutoLens can be found here <https://pyautolens.readthedocs.io/en/master/papers.html>_ .

Credits

Developers:

James Nightingale <https://github.com/Jammy2211>_ - Lead developer & PyAutoLens guru.

Richard Hayes <https://github.com/rhayes777>_ - Lead developer & PyAutoFit <https://github.com/rhayes777/PyAutoFit>_ guru.

Ashley Kelly <https://github.com/AshKelly>_ - Developer of pyquad <https://github.com/AshKelly/pyquad>_ for fast deflections computations.

Amy Etherington <https://github.com/amyetherington>_ - Magnification, Critical Curves and Caustic Calculations.

Xiaoyue Cao <https://github.com/caoxiaoyue>_ - Analytic Ellipitcal Power-Law Deflection Angle Calculations.

Qiuhan He - NFW Profile Lensing Calculations.

Nan Li <https://github.com/linan7788626>_ - Docker integration & support.

Code Donors:

Andrew Robertson <https://github.com/Andrew-Robertson>_ - Critical curve & caustic calculations.

Mattia Negrello - Visibility models in the uv-plane via direct Fourier transforms.

Andrea Enia <https://github.com/AndreaEnia>_ - Voronoi source-plane plotting tools.

Aristeidis Amvrosiadis <https://github.com/Sketos>_ - ALMA imaging data loading.

Conor O'Riordan - Broken Power-Law mass profile.

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