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Neural networks for image-based wavefront sensing for astronomy
Lund Observatory, Lund University.
Lund Observatory, Lund University.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
2019 (English)In: Optics Letters, ISSN 0146-9592, E-ISSN 1539-4794, Vol. 44, no 18, p. 4618-4621Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Optical Society of America, 2019. Vol. 44, no 18, p. 4618-4621
Keywords [en]
convolutional neural network, astronomical telescope, wavefront sensing, Zernike polynomials
National Category
Signal Processing
Research subject
Atmospheric science
Identifiers
URN: urn:nbn:se:ltu:diva-76039DOI: 10.1364/OL.44.004618ISI: 000485817500048Scopus ID: 2-s2.0-85072106843OAI: oai:DiVA.org:ltu-76039DiVA, id: diva2:1352117
Note

Validerad;2019;Nivå 2;2019-09-20 (johcin)

Available from: 2019-09-17 Created: 2019-09-17 Last updated: 2019-10-08Bibliographically approved

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Enmark, Anita

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