Machine learning wavefront sensing for the James Webb Space Telescope

Heriniaina F. Rajaoberison; Joseph S. Tang; James R. Fienup

doi:10.1117/12.2630621

27 August 2022 Machine learning wavefront sensing for the James Webb Space Telescope

Heriniaina F. Rajaoberison, Joseph S. Tang, James R. Fienup

Proceedings Volume 12180, Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave; 121806A (2022) https://doi.org/10.1117/12.2630621
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada

Conference Poster

Abstract

The fine alignment of the James Webb Space Telescope (JWST)’s 18 primary mirror segments relies on image-based wavefront sensing. Previously a convolutional neural network (CNN) produced phase estimates with around 0.37 waves RMS error, which were adequate as starting guesses for phase retrieval to accurately predict global Zernike coefficients. In contrast, our study uses a CNN to sense segment piston errors between the 18 segments. Our trained CNN model could retrieve segment piston phase errors of ±0.5 waves from a single defocused point-spread function to within 0.02 waves RMS, without the need of additional phase retrieval algorithms.

Conference Presentation

Citation Download Citation

Heriniaina F. Rajaoberison, Joseph S. Tang, and James R. Fienup "Machine learning wavefront sensing for the James Webb Space Telescope", Proc. SPIE 12180, Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave, 121806A (27 August 2022); https://doi.org/10.1117/12.2630621

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