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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Broadband Phase Retrieval for Image-Based Wavefront Sensing

A focus-diverse phase-retrieval algorithm has been shown to perform adequately for the purpose of image-based wavefront sensing when (1) broadband light (typically spanning the visible spectrum) is used in forming the images by use of an optical system under test and (2) the assumption of monochromaticity is applied to the broadband image data. Heretofore, it had been assumed that in order to obtain adequate performance, it is necessary to use narrowband or monochromatic light. Some background information, including definitions of terms and a brief description of pertinent aspects of image-based phase retrieval, is prerequisite to a meaningful summary of the present development. Phase retrieval is a general term used in optics to denote estimation of optical imperfections or aberrations of an optical system under test. The term image-based wavefront sensing refers to a general class of algorithms that recover optical phase information, and phase-retrieval algorithms constitute a subset of this class. In phase retrieval, one utilizes the measured response of the optical system under test to produce a phase estimate. The optical response of the system is defined as the image of a point-source object, which could be a star or a laboratory point source. The phase-retrieval problem is characterized as image-based in the sense that a charge-coupled-device camera, preferably of scientific imaging quality, is used to collect image data where the optical system would normally form an image. In a variant of phase retrieval, denoted phase-diverse phase retrieval [which can include focus-diverse phase retrieval (in which various defocus planes are used)], an additional known aberration (or an equivalent diversity function) is superimposed as an aid in estimating unknown aberrations by use of an image-based wavefront-sensing algorithm. Image-based phase-retrieval differs from such other wavefront-sensing methods, such as interferometry, shearing interferometry, curvature wavefront sensing, and Shack-Hartmann sensing, all of which entail disadvantages in comparison with image-based methods. The main disadvantages of these non-image based methods are complexity of test equipment and the need for a wavefront reference.

Dean, Bruce H.↗
NASA NTRS2004

Target Selection and Imaging Requirements for JWST Fine Phasing

To achieve and maintain the fine alignment of its segmented primary mirror the James Webb Space Telescope (JWST) plans to use focus-diverse wavefront sensing (WFS) techniques with science camera imagery. The optical requirements for JWST are such that the error contribution from the WFS itself must be limited 10nm rms over all the controllable degrees of freedom of the telescope. In this paper, we will explore the requirements on the target selection and imaging requirements necessary to achieve the desired level of WFS accuracy. Using Monte Carlo simulations we explore the WFS error as a function of wavefront aberrations level, defocus-diversity level, optical bandwidth and imaging signal-to-noise ratio to establish the key imaging requirements. By taking into account practical integration time limits along with the distribution of the defocused point-spread functions, we establish the bright and faint star magnitude limits suitable for WFS target selection.

wavefront sensing↗
NASA NTRS2007

Iterative-Transform Phase Retrieval Using Adaptive Diversity

A phase-diverse iterative-transform phase-retrieval algorithm enables high spatial-frequency, high-dynamic-range, image-based wavefront sensing. [The terms phase-diverse, phase retrieval, image-based, and wavefront sensing are defined in the first of the two immediately preceding articles, Broadband Phase Retrieval for Image-Based Wavefront Sensing (GSC-14899-1).] As described below, no prior phase-retrieval algorithm has offered both high dynamic range and the capability to recover high spatial-frequency components. Each of the previously developed image-based phase-retrieval techniques can be classified into one of two categories: iterative transform or parametric. Among the modifications of the original iterative-transform approach has been the introduction of a defocus diversity function (also defined in the cited companion article). Modifications of the original parametric approach have included minimizing alternative objective functions as well as implementing a variety of nonlinear optimization methods. The iterative-transform approach offers the advantage of ability to recover low, middle, and high spatial frequencies, but has disadvantage of having a limited dynamic range to one wavelength or less. In contrast, parametric phase retrieval offers the advantage of high dynamic range, but is poorly suited for recovering higher spatial frequency aberrations. The present phase-diverse iterative transform phase-retrieval algorithm offers both the high-spatial-frequency capability of the iterative-transform approach and the high dynamic range of parametric phase-recovery techniques. In implementation, this is a focus-diverse iterative-transform phaseretrieval algorithm that incorporates an adaptive diversity function, which makes it possible to avoid phase unwrapping while preserving high-spatial-frequency recovery. The algorithm includes an inner and an outer loop (see figure). An initial estimate of phase is used to start the algorithm on the inner loop, wherein multiple intensity images are processed, each using a different defocus value. The processing is done by an iterative-transform method, yielding individual phase estimates corresponding to each image of the defocus-diversity data set. These individual phase estimates are combined in a weighted average to form a new phase estimate, which serves as the initial phase estimate for either the next iteration of the iterative-transform method or, if the maximum number of iterations has been reached, for the next several steps, which constitute the outerloop portion of the algorithm. The details of the next several steps must be omitted here for the sake of brevity. The overall effect of these steps is to adaptively update the diversity defocus values according to recovery of global defocus in the phase estimate. Aberration recovery varies with differing amounts as the amount of diversity defocus is updated in each image; thus, feedback is incorporated into the recovery process. This process is iterated until the global defocus error is driven to zero during the recovery process. The amplitude of aberration may far exceed one wavelength after completion of the inner-loop portion of the algorithm, and the classical iterative transform method does not, by itself, enable recovery of multi-wavelength aberrations. Hence, in the absence of a means of off-loading the multi-wavelength portion of the aberration, the algorithm would produce a wrapped phase map. However, a special aberration-fitting procedure can be applied to the wrapped phase data to transfer at least some portion of the multi-wavelength aberration to the diversity function, wherein the data are treated as known phase values. In this way, a multiwavelength aberration can be recovered incrementally by successively applying the aberration-fitting procedure to intermediate wrapped phase maps. During recovery, as more of the aberration is transferred to the diversity function following successive iterations around the ter loop, the estimated phase ceases to wrap in places where the aberration values become incorporated as part of the diversity function. As a result, as the aberration content is transferred to the diversity function, the phase estimate resembles that of a reference flat.

Dean, Bruce H.↗
NASA NTRS2018

Picometer Differential Wavefront Metrology by Nonlinear Zernike Wavefront Sensing for LUVOIR

We propose the Nonlinear Zernike wavefront sensor (NLZWFS) for out-of-band differential wavefront sensing to augment primary mirror stability on LUVOIR and similar mission concepts during exoplanet coronagraphy. This new data analysis paradigm involving a full polychromatic scalar physical optics model for the phase-shifting Zernike wavefront sensor removes the linearity assumptions which would otherwise prevent accurate sensing. We show Monte-Carlo simulations of NLZWFS and focus-diverse phase retrieval to understand the exposure times necessary to achieve picometer-level stability in the telescope wavefront.

Redding, David C.↗
NASA NTRS2021

Characterizing Deformable Mirrors for the MagAO-X Instrument

The MagAO-X instrument is a new extreme adaptive optics system for high-contrast imaging at visibleand near-infrared wavelengths on the Magellan Clay Telescope. A central component of this system is a 2040-actuatormicroelectromechanical deformable mirror (DM) from Boston Micromachines Corp. that operates at 3.63 kHz forhigh-order wavefront control (the tweeter). Two additional DMs from ALPAO perform the low-order (the woofer)and non-common-path science-arm wavefront correction (the NCPC DM). Prior to integration with the instrument, wecharacterized these devices using a Zygo Verifire Interferometer to measure each DM surface. We present the resultsof the characterization effort here, demonstrating the ability to drive tweeter to a flat of 6.9 nm RMS surface (and 0.56nm RMS surface within its control bandwidth), the woofer to 2.2 nm root mean square (RMS) surface, and the NCPCDM to 2.1 nm RMS surface over the MagAO-X beam footprint on each device. Using focus-diversity phase retrievalon the MagAO-X science cameras to estimate the internal instrument wavefront error (WFE), we further show that theintegrated DMs correct the instrument WFE to 18.7 nm RMS, which, combined with a 11.7% pupil amplitude RMS,produces a Strehl ratio of 0.94 at H↵

Kyle Van Gorkom↗