Deep neural networks to improve the dynamic range of Zernike phase-contrast wavefront sensing in high-contrast imaging systems

Gregory Allan; Iksung Kang; Ewan S. Douglas; Mamadou N'Diaye; George Barbastathis; Kerri Cahoy

doi:10.1117/12.2562927

13 December 2020 Deep neural networks to improve the dynamic range of Zernike phase-contrast wavefront sensing in high-contrast imaging systems

Gregory Allan, Iksung Kang, Ewan S. Douglas, Mamadou N'Diaye, George Barbastathis, Kerri Cahoy

Author Affiliations +

Proceedings Volume 11443, Space Telescopes and Instrumentation 2020: Optical, Infrared, and Millimeter Wave; 1144349 (2020) https://doi.org/10.1117/12.2562927
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Conference Poster

Abstract

In high-contrast imaging applications, such as the direct imaging of exoplanets, a coronagraph is used to suppress the light from an on-axis star so that a dimmer, off-axis object can be imaged. To maintain a high-contrast dark region in the image, optical aberrations in the instrument must be minimized. The use of phase-contrast-based Zernike Wavefront Sensors (ZWFS) to measure and correct for aberrations has been studied for large segmented aperture telescopes and ZWFS are planned for the coronagraph instrument on the Roman Space Telescope (RST). ZWFS enable subnanometer wavefront sensing precision, but their response is nonlinear. Lyot-based Low-OrderWavefront Sensors (LLOWFS) are an alternative technique, where light rejected from a coronagraph's Lyot stop is used for linear measurement of small wavefront displacements. Recently, the use of Deep Neural Networks (DNNs) to enable phase retrieval from intensity measurements has been demonstrated in several optical configurations. In a LLOWFS system, the use of DNNs rather than linear regression has been shown to greatly extend the sensor's usable dynamic range. In this work, we investigate the use of two different types of machine learning algorithms to extend the dynamic range of the ZWFS. We present static and dynamic deep learning architectures for single- and multi-wavelength measurements, respectively. Using simulated ZWFS intensity measurements, we validate the network training technique and present phase reconstruction results. We show an increase in the capture range of the ZWFS sensor by a factor of 3.4 with a single wavelength and 4.5 with four wavelengths.

Conference Presentation

Citation Download Citation

Gregory Allan, Iksung Kang, Ewan S. Douglas, Mamadou N'Diaye, George Barbastathis, and Kerri Cahoy "Deep neural networks to improve the dynamic range of Zernike phase-contrast wavefront sensing in high-contrast imaging systems", Proc. SPIE 11443, Space Telescopes and Instrumentation 2020: Optical, Infrared, and Millimeter Wave, 1144349 (13 December 2020); https://doi.org/10.1117/12.2562927

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE