Wavefront prediction using artificial neural networks with CANARY telemetry

Xuewen Liu; Tim Morris; Lisa Bardou

doi:10.1117/12.2561693

13 December 2020 Wavefront prediction using artificial neural networks with CANARY telemetry

Xuewen Liu, Tim Morris, Lisa Bardou

Proceedings Volume 11448, Adaptive Optics Systems VII; 114484C (2020) https://doi.org/10.1117/12.2561693
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Abstract

In this paper we use artificial neural networks (ANNs) as a nonlinear wavefront predictor for CANARY open-loop telemetry. CANARY is a single channel multi-object adaptive optics demonstrator hosted by the 4.2 m William Herschel Telescope on La Palma island. These datasets were taken by the on-axis 7×7 NGS (natural guide star) Shack-Hartmann wavefront sensor between 28 September and 2 October, 2017. The ANN predictor is trained in simulations, assuming frozen flow turbulence. We show that the ANN predictor did not improve the system performance with a two-frame latency in terms of residual wavefront errors. Analyses with auto-covariance maps show that a stationary layer was observed by CANARY during those nights, indicative of strong dome seeing. This implies the need of a more representative turbulence model for training ANN predictors with non frozen flow observations.

Citation Download Citation

Xuewen Liu, Tim Morris, and Lisa Bardou "Wavefront prediction using artificial neural networks with CANARY telemetry", Proc. SPIE 11448, Adaptive Optics Systems VII, 114484C (13 December 2020); https://doi.org/10.1117/12.2561693

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