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We present recent results from the initial testing of an Artificial Neural Network (ANN) based tomographic reconstructor Complex Atmospheric Reconstructor based on Machine lEarNing (CARMEN) on Canary, an Adaptive Optics demonstrator operated on the 4.2m William Herschel Telescope, La Palma. The reconstructor was compared with contemporaneous data using the Learn and Apply (L&A) tomographic reconstructor. We find that the fully optimised L&A tomographic reconstructor outperforms CARMEN by approximately 5% in Strehl ratio or 15nm rms in wavefront error. We also present results for Canary in Ground Layer Adaptive Optics mode to show that the reconstructors are tomographic. The results are comparable and this small deficit is attributed to limitations in the training data used to build the ANN. Laboratory bench tests show that the ANN can out perform L&A under certain conditions, e.g. if the higher layer of a model two layer atmosphere was to change in altitude by ~300~m (equivalent to a shift of approximately one tenth of a subaperture).
CANARY is the multi-object adaptive optics (MOAO) on-sky pathfinder developed in the perspective of Multi-Object Spectrograph on Extremely Large Telescopes~(ELTs). In 2013, CANARY was operated on-sky at the William Herschel telescope~(WHT), using thr
The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplica
ESPRESSO is the new high-resolution spectrograph of ESOs Very-Large Telescope (VLT). It was designed for ultra-high radial-velocity precision and extreme spectral fidelity with the aim of performing exoplanet research and fundamental astrophysical ex
Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind i
Predictive wavefront control is an important and rapidly developing field of adaptive optics (AO). Through the prediction of future wavefront effects, the inherent AO system servo-lag caused by the measurement, computation, and application of the wav