Open-loop tomography with artificial neural networks on CANARY: on-sky results

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 optimized L&A tomographic reconstructor outperforms CARMEN by approximately 5percent 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 outperform L&A under certain conditions, e.g. if the higher layer of a model two layer atmosphere was to change in altitude by ∼300m (equivalent to a shift of approximately one tenth of a subaperture).

[1]  James Roger P. Angel,et al.  First Results of an On-Line Adaptive Optics System with Atmospheric Wavefront Sensing by an Artificial Neural Network , 1992 .

[2]  J. Angel,et al.  Adaptive optics for array telescopes using neural-network techniques , 1990, Nature.

[3]  Dani Guzman,et al.  Using artificial neural networks for open-loop tomography. , 2011, Optics express.

[4]  R. Q. Fugate,et al.  Use of a neural network to control an adaptive optics system for an astronomical telescope , 1991, Nature.

[5]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[6]  Lakhmi C. Jain,et al.  Neural Network Training Using Genetic Algorithms , 1996 .

[7]  V. S. Dhillon,et al.  Stereo-SCIDAR: optical turbulence profiling with high sensitivity using a modified SCIDAR instrument , 2013, 1312.3465.

[8]  Alastair Basden,et al.  The Durham adaptive optics real-time controller: capability and Extremely Large Telescope suitability , 2012, 1205.4532.

[9]  F. Roddier,et al.  Experimental determination of two-dimensional spatiotemporal power spectra of stellar light scintillation Evidence for a multilayer structure of the air turbulence in the upper troposphere , 1973 .

[10]  Francois Rigaut,et al.  Atmospheric turbulence profiling using multiple laser star wavefront sensors , 2012 .

[11]  M C Roggemann,et al.  Processing wave-front-sensor slope measurements using artificial neural networks. , 1996, Applied optics.

[12]  G. Rousset,et al.  Tomography approach for multi-object adaptive optics. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[14]  Richard W. Wilson,et al.  SLODAR: measuring optical turbulence altitude with a Shack–Hartmann wavefront sensor , 2002 .

[15]  E. Gendron,et al.  The FALCON concept: multi-object adaptive optics and atmospheric tomography for integral field spectroscopy - principles and performance on an 8-m telescope , 2006, astro-ph/0612538.

[16]  Timothy Butterley,et al.  Profiling the surface layer of optical turbulence with SLODAR , 2010 .

[17]  Goh Bee Hua.  Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regression , 1996 .

[18]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[19]  Joel L. Davis,et al.  An Introduction to Neural and Electronic Networks , 1995 .