Kernel Phase and Coronagraphy with Automatic Differentiation

Article


Pope, Benjamin J. S., Pueyo, Laurent, Xin, Yinzi and Tuthill, Peter G.. 2021. "Kernel Phase and Coronagraphy with Automatic Differentiation." The Astrophysical Journal: an international review of astronomy and astronomical physics. 907 (1), pp. 1-14. https://doi.org/10.3847/1538-4357/abcb00
Article Title

Kernel Phase and Coronagraphy with Automatic Differentiation

ERA Journal ID1057
Article CategoryArticle
AuthorsPope, Benjamin J. S. (Author), Pueyo, Laurent (Author), Xin, Yinzi (Author) and Tuthill, Peter G. (Author)
Journal TitleThe Astrophysical Journal: an international review of astronomy and astronomical physics
Journal Citation907 (1), pp. 1-14
Article Number40
Number of Pages14
Year2021
PublisherIOP Publishing
Place of PublicationUnited States
ISSN0004-637X
1538-4357
Digital Object Identifier (DOI)https://doi.org/10.3847/1538-4357/abcb00
Web Address (URL)https://iopscience.iop.org/article/10.3847/1538-4357/abcb00
Abstract

The accumulation of aberrations along the optical path in a telescope produces distortions and speckles in the resulting images, limiting the performance of cameras at high angular resolution. It is important to achieve the highest possible sensitivity to faint sources, using both hardware and data analysis software. While analytic methods are efficient, real systems are better modeled numerically, but numerical models of complicated optical systems with many parameters can be hard to understand, optimize, and apply. Automatic differentiation or 'backpropagation' software developed for machine-learning applications now makes calculating derivatives with respect to aberrations in arbitrary planes straightforward for any optical system. We apply this powerful new tool to the problem of high-angular-resolution astronomical imaging. Self-calibrating observables such as the 'closure phase' or 'bispectrum' have been widely used in optical and radio astronomy to mitigate optical aberrations and achieve high-fidelity imagery. Kernel phases are a generalization of closure phases valid in the limit of small phase errors. Using automatic differentiation, we reproduce existing kernel phase theory within this framework and demonstrate an extension to the case of a Lyot coronagraph, which is found to have self-calibrating combinations of speckles. which are resistant to phase noise, but only in the very high-wave-front-quality regime. As an illustrative example, we reanalyze Palomar adaptive optics observations of the binary α Ophiuchi, finding consistency between the new pipeline and the existing standard. We present a new Python package morphine that incorporates these ideas, with an interface similar to the popular package poppy, for optical simulation with automatic differentiation. These methods may be useful for designing improved astronomical optical systems by gradient descent.

KeywordsDirect imaging; Astronomy data analysis; Opticalinterferometry; Coronagraphic imaging; Astronomical simulations
ANZSRC Field of Research 2020510109. Stellar astronomy and planetary systems
Byline AffiliationsCentre for Astrophysics
Space Telescope Science Institute, United States
Massachusetts Institute of Technology, United States
University of Sydney
Institution of OriginUniversity of Southern Queensland
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