Deep residual learning for low-order wavefront sensing in high-contrast imaging systems
Name
oe-28-18-26267.pdf
Description
Published version
Size
2.39 MB
Format
Adobe PDF
Checksum (MD5)
e672867cd8eec52c3a3ede486bc6acd3
Author(s)
Allan, Gregory
Kang, Iksung
Douglas, Ewan S
Barbastathis, George
Cahoy, Kerri
Date Issued
2020
Journal
Optics Express
Publisher
The Optical Society
Version
Final published version
Abstract
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement Sensing and correction of low-order wavefront aberrations is critical for high-contrast astronomical imaging. State of the art coronagraph systems typically use image-based sensing methods that exploit the rejected on-axis light, such as Lyot-based low order wavefront sensors (LLOWFS); these methods rely on linear least-squares fitting to recover Zernike basis coefficients from intensity data. However, the dynamic range of linear recovery is limited. We propose the use of deep neural networks with residual learning techniques for non-linear wavefront sensing. The deep residual learning approach extends the usable range of the LLOWFS sensor by more than an order of magnitude compared to the conventional methods, and can improve closed-loop control of systems with large initial wavefront error. We demonstrate that the deep learning approach performs well even in low-photon regimes common to coronagraphic imaging of exoplanets.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1364/OE.397790
