Adaptive MGS Phase Retrieval software uses the Modified Gerchberg-Saxton (MGS) algorithm, an image-based sensing method that can turn any focal plane science instrument into a wavefront sensor, avoiding the need to use external metrology equipment. Knowledge of the wavefront enables intelligent control of active optical systems.
The software calculates the optical path difference (wavefront) errors in the exit pupil of an optical system using only intensity images of a point of light. The light input may be a star, laser, or any point source measured at symmetric positions about focus and at the pupil. As such, the software is a key enabling technology for space telescopes. With only a basic understanding of the optical system parameters (e.g. imaging wavelength, ƒ/number, measurement positions, etc.), the software evolves an internal model of the optical system to best match the data ensemble. Once optimized, the software proceeds to accurately estimate the wavefront of light as it travels through the optical system.
The MGS software is highly adaptable to a large range of optical systems and includes many innovative features. This version does not require an extensive and complete understanding of the system under test. Instead, using Automatic Model Adaptation, only the most basic system characteristics must be known. The algorithm adapts these parameters to best fit the data ensemble. These steps are crucial in achieving extremely high accuracy in the wavefront solution at the system exit-pupil. In addition, a convergence- monitoring feature allows the algorithm to stop when the wavefront solution has been reached to within a specified error tolerance level.
The software also facilitates the application of prior system knowledge to better deal with high-dynamic range wavefront errors. This is especially important where the error magnitude is much greater than the imaging wavelength (a significant problem in wavefront sensing). The software can use wavefront models based on previous runs or optical measurements, or predictions from external models, to initiate a prior phase estimate, through its Prior Phase Builder Graphical User Interface. The prior phase is treated by the software as a Numerical Nulling Reference, which is evolved in an outer-outer loop during computation, until it contains the full solution. The innermost iteration then has the simpler job of estimating the low-dynamic range residual difference of the true wavefront error from the Nulling Reference model. This allows the inner loop to operate around null, where it is most accurate and robust.
In addition to the wavefront solution, the software can provide an improved set of system parameters. For example, the result can report the true position of best focus and true ƒ/number in the optical system.
This program was written by Scott A. Basinger, Siddarayappa Bikkannavar, David Cohen, Joseph J. Green, John Lou, Catherine Ohara, David Redding, and Fang Shi of Caltech for NASA's Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-43857.
This Brief includes a Technical Support Package (TSP).
Adaptive MGS Phase Retrieval
(reference NPO-43857) is currently available for download from the TSP library.
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Overview
The document is a Technical Support Package from NASA's Jet Propulsion Laboratory (JPL) detailing the Adaptive Modified Gerchberg-Saxton (MGS) Phase Retrieval algorithm, which is crucial for wavefront sensing in advanced astronomical instruments. It is part of the NASA Tech Briefs series and is intended to disseminate information on aerospace-related developments with broader technological, scientific, or commercial applications.
The document outlines the algorithm's fundamentals, including its operational principles and the sources of bias that can affect wavefront sensing. It emphasizes the importance of mitigating these biases to enhance the accuracy of wavefront measurements, which are critical for the performance of space telescopes and other optical systems.
Key experimental validations of the algorithm are discussed, showcasing its application in various testbeds, including the James Webb Space Telescope (JWST) and the TPF (Terrestrial Planet Finder) High Contrast Imaging Testbed. The document highlights a specific experiment involving the TPF/HCIT WFS Repeatability Experiment, where datasets were collected over approximately 40 hours under controlled conditions, demonstrating the algorithm's robustness and reliability.
The document also touches on the concept of "diversity phase," which refers to the true phase estimate that is independent of the image and is derived from a combination of prior phase information and iterative processing. This aspect is crucial for improving the accuracy of phase retrieval in complex optical systems.
Overall, the Technical Support Package serves as a comprehensive resource for understanding the Adaptive MGS Phase Retrieval algorithm, its applications in wavefront sensing, and the experimental efforts undertaken to validate its effectiveness. It is a valuable document for researchers and engineers involved in the development of next-generation astronomical instruments, providing insights into the challenges and solutions associated with wavefront control in space-based observatories.
For further inquiries or detailed information, the document provides contact details for the Innovative Technology Assets Management office at JPL, encouraging collaboration and knowledge sharing within the aerospace community.
