Image-based wavefront sensing for astronomy using neural networks
(2020) In Journal of Astronomical Telescopes, Instruments, and Systems 6(3).- Abstract
- Motivated by the potential of nondiffraction limited, real-time computational image sharpening with neural networks in astronomical telescopes, we studied wavefront sensing with convolutional neural networks based on a pair of in-focus and out-of-focus point spread functions. By simulation, we generated a large dataset for training and validation of neural networks and trained several networks to estimate Zernike polynomial approximations for the incoming wavefront. We included the effect of noise, guide star magnitude, blurring by wide-band imaging, and bit depth. We conclude that the “ResNet” works well for our purpose, with a wavefront RMS error of 130 nm for r0 = 0.3 m, guide star magnitudes 4 to 8, and inference time of 8 ms. It... (More)
- Motivated by the potential of nondiffraction limited, real-time computational image sharpening with neural networks in astronomical telescopes, we studied wavefront sensing with convolutional neural networks based on a pair of in-focus and out-of-focus point spread functions. By simulation, we generated a large dataset for training and validation of neural networks and trained several networks to estimate Zernike polynomial approximations for the incoming wavefront. We included the effect of noise, guide star magnitude, blurring by wide-band imaging, and bit depth. We conclude that the “ResNet” works well for our purpose, with a wavefront RMS error of 130 nm for r0 = 0.3 m, guide star magnitudes 4 to 8, and inference time of 8 ms. It can also be applied for closed-loop operation in an adaptive optics system. We also studied the possible use of a Kalman filter or a recurrent neural network and found that they were not beneficial to the performance of our wavefront sensor. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/96b1b336-f865-4325-9b4c-7f53471adb65
- author
- Andersen, Torben LU ; Owner-Petersen, Mette LU and Enmark, Anita LU
- organization
- publishing date
- 2020-07-10
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Neural networks, Point spread functions, Wavefront sensors, Wavefronts, Stars, Astronomy, Telescopes, V-band, Error analysis
- in
- Journal of Astronomical Telescopes, Instruments, and Systems
- volume
- 6
- issue
- 3
- article number
- 034002
- pages
- 15 pages
- publisher
- SPIE
- external identifiers
-
- scopus:85090380932
- ISSN
- 2329-4124
- DOI
- 10.1117/1.JATIS.6.3.034002
- language
- English
- LU publication?
- yes
- id
- 96b1b336-f865-4325-9b4c-7f53471adb65
- date added to LUP
- 2020-07-13 23:27:42
- date last changed
- 2024-04-03 10:02:26
@article{96b1b336-f865-4325-9b4c-7f53471adb65, abstract = {{Motivated by the potential of nondiffraction limited, real-time computational image sharpening with neural networks in astronomical telescopes, we studied wavefront sensing with convolutional neural networks based on a pair of in-focus and out-of-focus point spread functions. By simulation, we generated a large dataset for training and validation of neural networks and trained several networks to estimate Zernike polynomial approximations for the incoming wavefront. We included the effect of noise, guide star magnitude, blurring by wide-band imaging, and bit depth. We conclude that the “ResNet” works well for our purpose, with a wavefront RMS error of 130 nm for r0 = 0.3 m, guide star magnitudes 4 to 8, and inference time of 8 ms. It can also be applied for closed-loop operation in an adaptive optics system. We also studied the possible use of a Kalman filter or a recurrent neural network and found that they were not beneficial to the performance of our wavefront sensor.}}, author = {{Andersen, Torben and Owner-Petersen, Mette and Enmark, Anita}}, issn = {{2329-4124}}, keywords = {{Neural networks; Point spread functions; Wavefront sensors; Wavefronts; Stars; Astronomy; Telescopes; V-band; Error analysis}}, language = {{eng}}, month = {{07}}, number = {{3}}, publisher = {{SPIE}}, series = {{Journal of Astronomical Telescopes, Instruments, and Systems}}, title = {{Image-based wavefront sensing for astronomy using neural networks}}, url = {{http://dx.doi.org/10.1117/1.JATIS.6.3.034002}}, doi = {{10.1117/1.JATIS.6.3.034002}}, volume = {{6}}, year = {{2020}}, }