Phase retrieval and design with automatic differentiation: tutorial

Article


Wong, Alison, Pope, Benjamin, Desdoigts, Louis, Tuthill, Peter, Norris, Barnaby and Betters, Chris. 2021. "Phase retrieval and design with automatic differentiation: tutorial." Journal of the Optical Society of America B: Optical Physics. 38 (9), pp. 2465-2478. https://doi.org/10.1364/JOSAB.432723
Article Title

Phase retrieval and design with automatic differentiation: tutorial

ERA Journal ID1260
Article CategoryArticle
AuthorsWong, Alison (Author), Pope, Benjamin (Author), Desdoigts, Louis (Author), Tuthill, Peter (Author), Norris, Barnaby (Author) and Betters, Chris (Author)
Journal TitleJournal of the Optical Society of America B: Optical Physics
Journal Citation38 (9), pp. 2465-2478
Number of Pages14
Year2021
Place of PublicationUnited States
ISSN0740-3224
1520-8540
Digital Object Identifier (DOI)https://doi.org/10.1364/JOSAB.432723
Web Address (URL)https://opg.optica.org/josab/fulltext.cfm?uri=josab-38-9-2465&id=455983
Abstract

The principal limitation in many areas of astronomy, especially for directly imaging exoplanets, arises from instability in the point spread function (PSF) delivered by the telescope and instrument. To understand the transfer function, it is often necessary to infer a set of optical aberrations given only the intensity distribution on the sensor—the problem of phase retrieval. This can be important for post-processing of existing data, or for the design of optical phase masks to engineer PSFs optimized to achieve high-contrast, angular resolution, or astrometric stability. By exploiting newly efficient and flexible technology for automatic differentiation, which in recent years has undergone rapid development driven by machine learning, we can perform both phase retrieval and design in a way that is systematic, user-friendly, fast, and effective. By using modern gradient descent techniques, this converges efficiently and is easily extended to incorporate constraints and regularization. We illustrate the wide-ranging potential for this approach using our new package, Morphine. Challenging applications performed with this code include precise phase retrieval for both discrete and continuous phase distributions, even where information has been censored such as heavily saturated sensor data. We also show that the same algorithms can optimize continuous or binary phase masks that are competitive with existing best solutions for two example problems: an apodizing phase plate coronagraph for exoplanet direct imaging, and a diffractive pupil for narrow-angle astrometry. The Morphine source code and examples are available open-source, with an interface similar to the popular physical optics package Poppy.

KeywordsAngular resolution; Automatic differentiations; Continuous phase; Flexible technologies; Gradient descent techniques; Intensity distribution; Narrow-angle astrometry; Phase retrieval
ANZSRC Field of Research 2020510109. Stellar astronomy and planetary systems
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Byline AffiliationsUniversity of Sydney
Centre for Astrophysics
Institution of OriginUniversity of Southern Queensland
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