Presentation + Paper
21 August 2020 A star-test wavefront sensor using neural network analysis
Author Affiliations +
Abstract
We describe a new, simple wavefront sensing method that uses a single measurement of a defocused star and a neural network to determine low-order wavefront components. The neural net is trained on computed diffracted star image data at 640 nm to output annular Zernike terms for an obscured circular aperture over a discrete range of all values. In the context of an actual star, the neural-net also provides the Fried’s parameter as an estimation of atmospheric turbulence. It is shown that the neural-net can produce a robust, high accuracy solution of the wavefront based on a single measurement. The method can also be used to simultaneously determine both on-axis and fielddependent wavefront performance from a single measurement of stars throughout the field. The prototype system can run at a rate of about 1 Hz with Python interpreted code, but higher speeds, up to video rates, are possible with compilation, proper hardware and optimization. This technique is particularly useful for low-order active-optics control and for optical alignment. A key advantage of this new method is that it only requires a single camera making it a simple cost-effective solution that can take advantage of an existing camera that may already be in an optical system. Results for this method are compared to high-precision interferometric data taken with a 4D Technology, PhaseCam interferometer and with an Innovations Foresight StarWave Shack Hartmann sensor from ALCOR SYSTEM under well-controlled conditions to validate performance. We also look at how the system has been implemented to use starlight for aligning multiple mirror telescopes in the presence of atmospheric seeing.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gaston Baudat and John B. Hayes "A star-test wavefront sensor using neural network analysis", Proc. SPIE 11490, Interferometry XX, 114900U (21 August 2020); https://doi.org/10.1117/12.2568018
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KEYWORDS
Artificial intelligence

Wavefronts

Wavefront sensors

Stars

Mirrors

Cameras

Monochromatic aberrations

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