TL;DR
This paper introduces Morphine, a new software package leveraging automatic differentiation and gradient descent for efficient phase retrieval and optical design, applicable to astronomy and exoplanet imaging.
Contribution
It presents a systematic, flexible, and fast approach for phase retrieval and design using modern automatic differentiation, outperforming traditional methods.
Findings
Effective phase retrieval for discrete and continuous phase distributions.
Optimized phase masks for exoplanet imaging and astrometry.
Open-source implementation with competitive results.
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…
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