Open-loop tomography with artificial neural networks on CANARY: on-sky results
arXiv:1405.6862 · doi:10.1093/mnras/stu758
Abstract
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).
8 pages, 6 figures
References in corpus (5)
- Stereo-SCIDAR: Optical turbulence profiling with high sensitivity using a modified SCIDAR instrument
- Using artificial neural networks for open-loop tomography
- Atmospheric turbulence profiling using multiple laser star wavefront sensors
- The Durham adaptive optics real-time controller: Capability and ELT suitability
- The FALCON concept: multi-object adaptive optics and atmospheric tomography for integral field spectroscopy. Principles and performances on an 8 meter telescope
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