WaveDiff

A differentiable data-driven wavefront-based PSF modelling framework.

This repository includes:

• A differentiable PSF model entirely built in Tensorflow.
• A numpy-based PSF simulator here.
• All the scripts, jobs and notebooks required to reproduce the results in arXiv:2203.04908 and arXiv:2111.12541.

For more information on how to use the WaveDiff model through configurable scripts see the long-runs directory's README.

Proposed framework

A schematic of the proposed framework can be seen below. The PSF model is estimated (trained) using star observations in the field-of-view.

Install

wf-psf is pure python and can be easily installed with pip. After cloning the repository, run the following commands:

$ cd wf-psf
$ pip install .

The package can then be imported in Python as import wf_psf as wf. We recommend using the release 1.2.0 for stability as the current main branch is under development.

Requirements

• numpy [>=1.19.2]
• scipy [>=1.5.2]
• TensorFlow [==2.4.1]
• TensorFlow Addons [==0.12.1]
• Astropy [==4.2]
• zernike [==0.0.31]
• opencv-python [>=4.5.1.48]
• pillow [>=8.1.0]
• galsim [>=2.3.1]

Optional packages:

• matplotlib [=3.3.2]
• seaborn [>=0.11]

Reproducible research

arXiv:2203.04908 Rethinking data-driven point spread function modeling with a differentiable optical model (2022)

Submitted.

• Use the release 1.2.0.
• All the scripts, jobs and notebooks to reproduce the figures from the article can be found here.
• The trained PSF models are found here.
• The input PSF field can be found here.
• The script used to generate the input PSF field is this one.
• The code required to run the comparison against pixel-based PSF models is in this directory.
• The training of the models was done using this script. In order to match the script's option for the different models with the article you should follow:
  • poly->WaveDiff-original
  • graph->WaveDiff-graph
  • mccd->WaveDiff-Polygraph

Note: To run the comparison to other PSF models you need to install them first. See RCA, PSFEx and MCCD.

arXiv:2111.12541 Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model (2021)

NeurIPS 2021 Workshop on Machine Learning and the Physical Sciences.

• Use the release 1.2.0.
• All the scripts, jobs and notebooks to reproduce the figures from the article can be found here.

Citation

If you use wf-psf in a scientific publication, we would appreciate citations to the following paper:

Rethinking data-driven point spread function modeling with a differentiable optical model, T. Liaudat, J.-L. Starck, M. Kilbinger, P.-A. Frugier, arXiv:2203.04908, 2022.

The BibTeX citation is the following:

@misc{https://doi.org/10.48550/arxiv.2203.04908,
  doi = {10.48550/ARXIV.2203.04908},
  
  url = {https://arxiv.org/abs/2203.04908},
  
  author = {Liaudat, Tobias and Starck, Jean-Luc and Kilbinger, Martin and Frugier, Pierre-Antoine},
  
  keywords = {Instrumentation and Methods for Astrophysics (astro-ph.IM), Computer Vision and Pattern Recognition (cs.CV), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Rethinking data-driven point spread function modeling with a differentiable optical model},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}