Improving Astronomy Image Quality Through Real-Time Wavefront Estimation

David Thomas, Joshua Meyers, Steven M. Kahn

CVPRW 2021 pp. 2076-2085

doi:10.1109/CVPRW53098.2021.00236 /cvprw/2021/thomas2021cvprw-improving/

Abstract

We present a new framework for detecting telescope optics aberrations in real-time. The framework divides the problem into two subproblems that are highly amenable to machine learning and optimization. The first involves making local wavefront estimates with a convolutional neural network. The second involves interpolating the optics wavefront from all the local estimates by minimizing a convex loss function. We test our framework with simulations of the Vera Rubin Observatory. In a realistic mini-survey, the algorithm reduces the total magnitude of the optics wavefront by 66%, the optics PSF FWHM by 27%, and increases the Strehl ratio by a factor of 6. The resulting sharper images have the potential to boost the scientific payload for astrophysics and cosmology.

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Cite

Text

Thomas et al. "Improving Astronomy Image Quality Through Real-Time Wavefront Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00236

Markdown

[Thomas et al. "Improving Astronomy Image Quality Through Real-Time Wavefront Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/thomas2021cvprw-improving/) doi:10.1109/CVPRW53098.2021.00236

BibTeX

@inproceedings{thomas2021cvprw-improving,
  title     = {{Improving Astronomy Image Quality Through Real-Time Wavefront Estimation}},
  author    = {Thomas, David and Meyers, Joshua and Kahn, Steven M.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2021},
  pages     = {2076-2085},
  doi       = {10.1109/CVPRW53098.2021.00236},
  url       = {https://mlanthology.org/cvprw/2021/thomas2021cvprw-improving/}
}