Browsing by Author "Osborn, J."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    An integrated MASS/DIMM monitor based on a low-noise CCD detector
    (2015) Guesalaga Meissner, Andrés Rodrigo; Osborn, J.; Sarazin, M.; Neichel, B.; Perera, S.; Wilson, R.; Wizinowich, Peter
    We propose a novel design for a turbulence profiler. Using a single detector, images of the pupil (scintillation) and stars (image motion) are formed in the detector plane. The instrument is called FASS (Full Aperture Scintillation Sensor), as it uses the full aperture of the telescope. Different processing strategies are evaluated, including spatial segmentation and Fourier analysis. The different approaches are tested via simulation and on-sky data from two telescopes and compared to profiles obtained with the Durham Stereo-SCIDAR monitor. Overall, simulations shows that the method is more accurate that the classical MASS configuration, but it is shown that the photon noise plays an important role in the accuracy of the method, imposing stringent requirements on the pixel size, which must be significantly smaller than the speckle size formed from turbulence close to the ground (Fresnel law for speckle size).
  • Thumbnail Image
    Item
    Online estimation of the wavefront outer scale profile from adaptive optics telemetry
    (2017) Guesalaga Meissner, Andrés; Neichel, B.; Correia, C.; Butterley, T.; Osborn, J.; Masciadri, E.; Fusco, T.; Sauvage, J.
  • No Thumbnail Available
    Item
    Open-loop tomography with artificial neural networks on CANARY: on-sky results
    (2014) Osborn, J.; Guzman, D.; de Cos Juez, F. J.; Basden, A. G.; Morris, T. J.; Gendron, E.; Butterley, T.; Myers, R. M.; Guesalaga, A.; Sanchez Lasheras, F.; Gomez Victoria, M.; Sanchez Rodriguez, M. L.; Gratadour, D.; Rousset, G.
    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 4.2 m 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 optimized L&A tomographic reconstructor outperforms CARMEN by approximately 5 per cent in Strehl ratio or 15 nm 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 outperform L&A under certain conditions, e.g. if the higher layer of a model two layer atmosphere was to change in altitude by similar to 300 m (equivalent to a shift of approximately one tenth of a subaperture).