Neural networks for image-based wavefront sensing for astronomy

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • Luleå University of Technology
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Astronomi, astrofysik och kosmologi
Originalspråkengelska
Sidor (från-till)4618-4621
Antal sidor4
TidskriftOptics Letters
Volym44
Utgåva nummer18
StatusPublished - 2019 sep 13
PublikationskategoriForskning
Peer review utfördJa

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