Dynamic Artificial Neural Networks for Centroid Prediction in Astronomy

Weddell,; Webb,

doi:10.1109/his.2006.264951

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…A method will be de- scribed in this section that utilizes an echo state network (ESN) to predict phase distortions represented by multiple PSFs over a wide FOV. A method using a time-delayed neural network for prediction of aberrations caused by turbulence has been proposed [23]. However, in our view, the complexities in training and implementing such networks can be avoided with the use of ESNs.…”

Section: Reservoir Computingmentioning

confidence: 99%

Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function

Weddell

Webb

2008

IEEE J. Sel. Top. Signal Process.

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Abstract-A new method is presented which provides prediction of the spatially variant point spread function for the restoration of astronomical images, distorted by atmospheric turbulence when viewed using ground-based telescopes. Our approach uses reservoir computing to firstly learn the spatio-temporal evolution of aberrations caused by turbulence, and secondly, predicts the spacevarying point spread function (PSF) for application of widely-used deconvolution algorithms, resulting in the restoration of astronomical images. In this article, a reservoir-based, recurrent neural network is used to predict modal aberrations that comprise the spatially variant PSF over a wide field-of-view using a time-series ensemble from multiple reference beacons.

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Section: Reservoir Computingmentioning

confidence: 99%

Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function

Weddell

Webb

2008

IEEE J. Sel. Top. Signal Process.

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“…An accurate DM model is required for open-loop AO as the DM is not seen by the WFS. The difference between our proposal and the work of Lloyd-Hart & McGuire (1995) [12] and Weddell & Webb (2006) [13] is that we will train the network in simulation rather than on-sky. This allows us to select and control what the network learns and means that we can predict to a higher order.…”

Section: Introductionmentioning

confidence: 98%

“…The AO latency is then reduced allowing for a better correction. Weddell & Webb (2006 [13,14] developed this idea and used off-axis WFS measurements to temporally predict the onaxis slopes. [13] is that we will train the network in simulation rather than on-sky.…”

Section: Introductionmentioning

confidence: 99%

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Using artificial neural networks for open-loop tomography

Osborn¹,

Juez²,

Guzmán³

et al. 2012

Opt. Express

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Abstract:Modern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide sources that are used to sample the atmosphere. Multi-object adaptive optics (MOAO) is one such technique.Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. We compare our ANN method with a standard least squares type matrix multiplication method and to the learn and apply method developed for the CANARY MOAO instrument. The ANN is trained with a large range of possible turbulent layer positions and therefore does not require any input of the optical turbulence profile. It is therefore less susceptible to changing conditions than some existing methods. We also exploit the non-linear response of the ANN to make it more robust to noisy centroid measurements than other linear techniques. Assémat, E. Gendron, and F. Hammer, "The FALCON concept: multi-object adaptive optics and atmospheric tomography for integral field spectroscopy -principles and performance on an 8-m telescope," MNRAS 376, 287-312 (2007). 5. Morris, T., Hubert, Z., Myers, R., Gendron, E., Longmore, A., Rousset, G., Talbot, G., Fusco, T., Dipper, N., Vidal, F., Henry, D., Gratadour, D., Butterley, T., Chemla, F., Guzman, D., Laporte, P., Younger, E., Kellerer, A., Harrison, M., Marteaud, M., Geng, D., Basden, A., Guesalaga, A., Dunlop, C., Todd, S., Robert, C., Dee, K., Dickson, C., Vedrenne, N., Greenaway, A., Stobie, B., Dalgarno, H., and Skvarc, J., "CANARY: The NGS/LGS MOAO demonstrator for EAGLE," 1st AO4ELT conference p. 08003 (2010). 2527-2538 (2001). 18. J. W. Wild, E. J. Kibblewhite, and R. Vuilleumier, "Sparse matrix wave-front estimators for adaptive-optics systems for large ground-based telescopes," Opt. Lett. 20(9), 955 -957 (1995). 19. E. Thiébaut and M. Tallon, "Fast minimum variance wavefront reconstruction for extremely large telescopes," J.

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