Neural networks for image-based wavefront sensing for astronomy

Document identifier: oai:DiVA.org:ltu-76039
Access full text here:10.1364/OL.44.004618
Keyword: Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Signal Processing, Teknik och teknologier, Elektroteknik och elektronik, Signalbehandling, Convolutional neural network, Astronomical telescope, Wavefront sensing, Zernike polynomials, Atmospheric science, Atmosfärsvetenskap
Publication year: 2019
Abstract:

We study the possibility of using convolutional neural networks for wavefront sensing from a guide star image in astronomical telescopes. We generated a large number of artificial atmospheric wavefront screens and determined associated best-fit Zernike polynomials. We also generated in-focus and out-of-focus point-spread functions. We trained the well-known “Inception” network using the artificial data sets and found that although the accuracy does not permit diffraction-limited correction, the potential improvement in the residual phase error is promising for a telescope in the 2–4 m class.

Authors

Torben Andersen

Lund Observatory, Lund University
Other publications >>

Mette Owner-Petersen

Lund Observatory, Lund University
Other publications >>

Anita Enmark

Luleå tekniska universitet; Rymdteknik
Other publications >>

Record metadata

Click to view metadata