Turbulence profiling neural networks using imaging Shack-Hartmann data for wide-field image correction

R. J. Hamilton; Michael Hart

doi:10.1117/12.2628624

29 August 2022 Turbulence profiling neural networks using imaging Shack-Hartmann data for wide-field image correction

R. J. Hamilton, Michael Hart

Proceedings Volume 12185, Adaptive Optics Systems VIII; 121855T (2022) https://doi.org/10.1117/12.2628624
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada

Conference Poster

Abstract

Wide-field image correction of turbulence-induced phase requires tomographic reconstruction of each layer of turbulence. Before reconstruction can occur, the layers must be counted and ranged. A new signal-to-noise ratio metric for detecting a single layer of turbulence in a multi-layer atmosphere from SLOpe Detection And Ranging (SLODAR) measurements of Shack-Hartmann wave-front sensor (SHWFS) data is presented. 12,000 1-4 layer atmosphere profiles are procedurally defined by Fried length, layer altitude, and a minimum layer SNR requirement. Each profile is measured in simulation by a SHWFS in a 1.5 meter telescope with a 2.5 arcminute field of view over a 200 millisecond window. The simulation outputs are used as a 5-fold cross validation training data set for convolutional neural networks (CNNs) that count and range layers. The counting network achieved 92.6% accuracy and all ranging networks scored above 97.8% validation accuracy. We find that layers with SNR below 1 accounted for a majority of the misclassified points for all networks. We conclude that CNNs are a good candidate for wide-field image correction systems imaging through turbulence due to their ability to accurately profile the atmosphere from short time windows of collected data.

Citation Download Citation

R. J. Hamilton and Michael Hart "Turbulence profiling neural networks using imaging Shack-Hartmann data for wide-field image correction", Proc. SPIE 12185, Adaptive Optics Systems VIII, 121855T (29 August 2022); https://doi.org/10.1117/12.2628624

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