https://github.com/sibirrer/psfr

Point Spread Function reconstruction
https://github.com/sibirrer/psfr

astronomy lenstronomy psf-estimation

Last synced: 3 months ago
JSON representation

Point Spread Function reconstruction

• Host: GitHub
• URL: https://github.com/sibirrer/psfr
• Owner: sibirrer
• License: bsd-3-clause
• Created: 2022-07-17T22:06:48.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-08-22T16:49:00.000Z (almost 2 years ago)
• Last Synced: 2025-12-02T07:59:45.422Z (7 months ago)
• Topics: astronomy, lenstronomy, psf-estimation
• Language: Jupyter Notebook
• Homepage:
• Size: 8.75 MB
• Stars: 30
• Watchers: 3
• Forks: 9
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Authors: AUTHORS.rst

Awesome Lists containing this project

README

          
===========================================

PSFr - Point Spread Function reconstruction

===========================================

|PyPI| |CI| |Docs| |Coveralls|

.. |CI| image:: https://github.com/sibirrer/psfr/workflows/Tests/badge.svg

    :target: https://github.com/sibirrer/psfr/actions

.. |Docs| image:: https://readthedocs.org/projects/psfr/badge/?version=latest

        :target: http://psfr.readthedocs.io/en/latest/?badge=latest

        :alt: Documentation Status

.. |Coveralls| image:: https://coveralls.io/repos/github/sibirrer/psfr/badge.svg?branch=main

    :target: https://coveralls.io/github/sibirrer/psfr?branch=main

.. |PyPI| image:: https://img.shields.io/pypi/v/psfr?label=PyPI&logo=pypi

    :target: https://pypi.python.org/pypi/psfr

.. image:: https://github.com/sibirrer/psfr/blob/main/docs/_static/stacked_psf_animation.gif

Point Spread Function reconstruction for astronomical

ground- and space-based imaging data.

Example

-------

.. code-block:: python

    # get cutout stars in the field of a JWST observation (example import)

    from psfr.util import jwst_example_stars

    star_list_jwst = jwst_example_stars()

    # run PSF reconstruction (see documentation for further options)

    from psfr.psfr import stack_psf

    psf_model, center_list, mask_list = stack_psf(star_list_jwst, oversampling=4,

                                                  saturation_limit=None, num_iteration=50)

We further refer to the example Notebook_ and the Documentation_.

.. _Notebook: https://github.com/sibirrer/psfr/blob/main/notebooks/JWST_PSF_reconstruction.ipynb

.. _Documentation: https://psfr.readthedocs.io/en/latest/

Features

--------

* Iterative PSF reconstruction given cutouts of individual stars or other point-like sources.

* Sub-pixel astrometric shifts calculated and accounted for while performing the PSF reconstruction.

* PSF reconstruction available in super-sampling resolution.

* Masking pixels, saturation levels and other options to deal with artifacts in the data.

Algorithm

---------

The algorithm to iteratively propose a (optionally oversampled) PSF from a set of star cutouts goes as follow:

(1) Stack all the stars for an initial guess of the PSF on the centroid pixel (ignoring sub-pixel offsets)

(2) Fit the subpixel centroid with the PSF model estimate

(3) Shift PSF with sub-pixel interpolation to the sub-pixel position of individual stars

(4) Retrieve residuals of the shifted PSF model relative to the data of the cutouts

(5) Apply an inverse sub-pixel shift of the residuals to be focused on the center of the pixel

(6) Based on the inverse shifted residuals of a set of fixed stars, propose a correction to the previous PSF model

(7) Repeat step (3) - (6) multiple times with the option to repeat step (2)

Details and options for the different steps can be found in the documentation and the source code.

Used by

-------

PSFr is in use with James Webb Space Telescope imaging data (i.e., `Santini et al. 2022  `_,

`Merlin et al. 2022  `_,

`Yang et al. 2022  `_,

`Ding et al. 2022  `_).

The iterative PSF reconstruction procedure was originally developed and used for analyzing strongly lensed quasars

(i.e., `Birrer et al. 2019 `_

, `Shajib et al. 2018 `_ ,

`Shajib et al. 2019 `_ ,

`Schmidt et al. 2022 `_).

Other resources

---------------

We also refer to the astropy core package

`photutils `_

and in particular to the empirical PSF module

`ePSF `_ .

PSF reconstructions are e.g. reported by

`Anderson and King (2000; PASP 112, 1360) `_

and

`Anderson (2016), ISR WFC3 2016-12 `_.

Credits

-------

The software is an off-spring of the iterative PSF reconstruction scheme of `lenstronomy `_, in particular its `psf_fitting.py `_ functionalities.

If you make use of this software, please cite: 'This code is using PSFr (Birrer et al. in prep) utilizing features of

lenstronomy (`Birrer et al. 2021 `_)'.