Closed Loop Predictive Control of Adaptive Optics Systems with Convolutional Neural Networks

TL;DR

This paper introduces a novel deep neural network approach using adversarial priors for predictive control in adaptive optics, significantly improving image quality and faint guide star performance in simulated closed-loop systems.

Contribution

It presents a new method for training neural networks for predictive adaptive optics control using adversarial priors, demonstrating closed-loop performance improvements over classical methods.

Findings

Over 55% Strehl improvement for faint guide stars

LSTM models excel in high-contrast scenarios

Feedforward models perform better in high-noise conditions

Abstract

Predictive wavefront control is an important and rapidly developing field of adaptive optics (AO). Through the prediction of future wavefront effects, the inherent AO system servo-lag caused by the measurement, computation, and application of the wavefront correction can be significantly mitigated. This lag can impact the final delivered science image, including reduced strehl and contrast, and inhibits our ability to reliably use faint guidestars. We summarize here a novel method for training deep neural networks for predictive control based on an adversarial prior. Unlike previous methods in the literature, which have shown results based on previously generated data or for open-loop systems, we demonstrate our network's performance simulated in closed loop. Our models are able to both reduce effects induced by servo-lag and push the faint end of reliable control with natural guidestars, improving K-band Strehl performance compared to classical methods by over 55% for 16th magnitude guide stars on an 8-meter telescope. We further show that LSTM based approaches may be better suited in high-contrast scenarios where servo-lag error is most pronounced, while traditional feed forward models are better suited for high noise scenarios. Finally, we discuss future strategies for implementing our system in real-time and on astronomical telescope systems.