PyAutoLens

PyAutoLens: Open-Source Strong Gravitational Lensing - Published in JOSS (2021)

https://github.com/jammy2211/pyautolens

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PyAutoLens: Open Source Strong Gravitational Lensing

README.rst

PyAutoLens: Open-Source Strong Lensing
======================================

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`Installation Guide `_ |
`readthedocs `_ |
`Introduction on Binder `_ |
`HowToLens `_

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  :width: 900

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.

Getting Started
---------------

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
-------

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.

HowToLens
---------

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 `_

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`` ``MassProfile`` lenses a background source at redshift 1.0 with an
``Exponential`` ``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.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``.

.. 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.
    """
    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())

Owner

JOSS Publication

PyAutoLens: Open-Source Strong Gravitational Lensing

Published

February 20, 2021

DOI

10.21105/joss.02825

Volume 6, Issue 58, Page 2825

Authors

James. W. Nightingale
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Richard G. Hayes
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Ashley Kelly
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Aristeidis Amvrosiadis
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Amy Etherington
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Qiuhan He
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Nan Li
Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China

XiaoYue Cao
National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012, China

Jonathan Frawley
Advanced Research Computing, Durham University, Durham DH1 3LE

Shaun Cole
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Andrea Enia
Dipartimento di Fisica e Astronomia, Università degli Studi di Bologna, Via Berti Pichat 6/2, I-40127 Bologna, Italy

Carlos S. Frenk
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

David R. Harvey
Lorentz Institute, Leiden University, Niels Bohrweg 2, Leiden, NL-2333 CA, The Netherlands

Ran Li
National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012, China

Richard J. Massey
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Mattia Negrello
School of Physics and Astronomy, Cardiff University, The Parade, Cardiff CF24 3AA, UK

Andrew Robertson
Institute for Computational Cosmology, Stockton Rd, Durham DH1 3LE

Editor

Juanjo Bazán

Tags

astronomy gravitational lensing galaxies cosmology

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Citation (CITATIONS.rst)

.. _references:

Citations & References
======================

The bibtex entries for **PyAutoLens** and its affiliated software packages can be found
`here <https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.bib>`_, with example text for citing **PyAutoLens**
in `.tex format here <https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.tex>`_ format here and
`.md format here <https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.md>`_. As shown in the examples, we
would greatly appreciate it if you mention **PyAutoLens** by name and include a link to our GitHub page!

**PyAutoLens** is published in the `Journal of Open Source Software <https://joss.theoj.org/papers/10.21105/joss.02825#>`_ and its
entry in the above .bib file is under the citation key ``pyautolens``. Please also cite the MNRAS AutoLens
papers (https://academic.oup.com/mnras/article/452/3/2940/1749640 and https://academic.oup.com/mnras/article-abstract/478/4/4738/5001434?redirectedFrom=fulltext) which are included
under the citation keys ``Nightingale2015`` and ``Nightingale2018``.

You should also specify the non-linear search(es) you use in your analysis (e.g. Dynesty, Emcee, PySwarms, etc) in
the main body of text, and delete as appropriate any packages your analysis did not use. The citations.bib file includes
the citation key for all of these projects.

If you use decomposed mass models (e.g. stellar mass models like an ``Sersic`` or dark matter models like
an ``NFW``) please cite the following paper https://arxiv.org/abs/2106.11464 under
citation key ``Oguri2021``. Our deflection angle calculations are based on this method.

If you specifically use a decomposed mass model with the ``GeneralizedNFW`` please cite the following paper https://academic.oup.com/mnras/article/488/1/1387/5526256 under
citation key ``Anowar2019``.

The citations.bib file above also includes my work on `using strong lensing to study galaxy structure
<https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.2049N/abstract>`_. If you're feeling kind, please go ahead and stick
a citation in your introduction using \citep{Nightingale2019} or [@Nightingale2019] ;).

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