2022
DOI: 10.48550/arxiv.2205.07554
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J. Nousiainen,
C. Rajani,
M. Kasper
et al.

Abstract: Context. The direct imaging of potentially habitable Exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilohertz to several kilohertz. Most of the habitable exoplanets are located at small angular separations from their host stars, where the current con… Show more

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Cited by 2 publications
(51 citation statements)
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“…24 MagAO-X can also be used for remote research and development, as demonstrated by the testing of a reinforcement learning algorithm. 25 MagAO-X is also part of the High-Contrast Adaptive Optics Testbed (HCAT) for the Giant Magellan Telescope (GMT), part of the overall program to develop segment phase sensing and control strategies for the GMT. 7 As part of HCAT MagAO-X serves as the AO system and houses phase sensing and control experiments.…”
Section: Lab Testbedmentioning
confidence: 99%
“…24 MagAO-X can also be used for remote research and development, as demonstrated by the testing of a reinforcement learning algorithm. 25 MagAO-X is also part of the High-Contrast Adaptive Optics Testbed (HCAT) for the Giant Magellan Telescope (GMT), part of the overall program to develop segment phase sensing and control strategies for the GMT. 7 As part of HCAT MagAO-X serves as the AO system and houses phase sensing and control experiments.…”
Section: Lab Testbedmentioning
confidence: 99%
“…[20][21][22] A promising area of research for mitigating these nonlinearities is the use of neural networks for learning a nonlinear mapping between wavefront sensor measurements and wavefront, [23][24][25][26] or for nonlinear control. [27][28][29][30] Furthermore, the similarities between optical systems and Neural Networks have lead to studies exploiting automatic differentiation algorithms, initially developed for training NNs, for optimizing elements in the optical system 31,32 or more efficient wavefront control. 33,34 Automatic differentiation allows us to obtain gradients with respect to the free design parameters, even for complex optical systems with multiple elements and planes.…”
Section: Introductionmentioning
confidence: 99%