Neural networks for image-based wavefront sensing for astronomy
(2019) In Optics Letters 44(18). p.4618-4621- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/da9ed7a8-4f39-4226-ae5d-27f4fb247997
- author
- Andersen, Torben LU ; Owner-Petersen, Mette LU and Enmark, Anita LU
- organization
- publishing date
- 2019-09-13
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Optics Letters
- volume
- 44
- issue
- 18
- pages
- 4 pages
- publisher
- Optical Society of America
- external identifiers
-
- scopus:85072106843
- pmid:31517947
- ISSN
- 0146-9592
- DOI
- 10.1364/OL.44.004618
- language
- English
- LU publication?
- yes
- additional info
- Has been designated as "Editor's Pick" that serve to highlight articles with excellent scientific quality and that are representative of the work taking place in a specific field.
- id
- da9ed7a8-4f39-4226-ae5d-27f4fb247997
- date added to LUP
- 2019-09-28 22:31:47
- date last changed
- 2024-01-16 11:33:03
@article{da9ed7a8-4f39-4226-ae5d-27f4fb247997, 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.}}, author = {{Andersen, Torben and Owner-Petersen, Mette and Enmark, Anita}}, issn = {{0146-9592}}, language = {{eng}}, month = {{09}}, number = {{18}}, pages = {{4618--4621}}, publisher = {{Optical Society of America}}, series = {{Optics Letters}}, title = {{Neural networks for image-based wavefront sensing for astronomy}}, url = {{http://dx.doi.org/10.1364/OL.44.004618}}, doi = {{10.1364/OL.44.004618}}, volume = {{44}}, year = {{2019}}, }