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On-sky Static Aberration Estimation at W. M. Keck Observatory

Presentation #244.05 in the session New Approaches — iPoster Session.

Published onJun 29, 2022
On-sky Static Aberration Estimation at W. M. Keck Observatory

Over the past few decades, adaptive optics (AO) technology has made significant strides in improving the image quality of ground-based observations, however, accurately determining an image’s point spread function (PSF) remains challenging due to its temporal and spatial variability. An incomplete knowledge of the PSF severely limits the precision in photometric and astrometric measurements. PSF reconstruction is a technique that characterizes and mitigates the sources of error within a PSF solely from the telescope instrument data. While temporal error terms can be difficult to measure due to rapidly changing initial conditions, the static error terms can be measured, making them ideal conditions to evaluate. We attempt to quantify and mitigate static aberrations within the Keck AO system for use in PSF reconstruction using a phase diversity algorithm that inputs a pair of focused and defocused PSFs and outputs a reconstructed phase map — an image used to identify defects on an optical surface. We reanalyzed seven years of phase retrieval measurements uniformly, optimizing the diversity algorithm’s convergence threshold, evaluating the algorithm over a range of atmospheric conditions, and examining the impacts of edge artifact correction on the data. We found no correlation between atmospheric seeing conditions and wavefront error in the reconstructed phase maps, allowing us to reduce on-sky error calibration time, as there is no need to wait for ideal seeing conditions before taking a pair of measurements. We also found no abnormalities in the optical system, suggesting that wavefront uncertainties are largely caused by inherent limitations in the diversity algorithm itself. This work provides us with a better understanding of the static error terms affecting the PSF and paves the way for future research into how the diversity algorithm responds under various noise conditions.

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