Closing the loop on-sky with a vector-Zernike wavefront sensor using a convolutional neural network as phase reconstructor

Abstract

Context: The new giant segmented mirror telescopes will use adaptive optics to reach the fundamental limits in resolving power. To accomplish this, new wavefront sensors (WFS) have been designed to fulfill the requirements, but they may require non-linear reconstruction techniques to operate given the nature of the signal of the WFSs Aims: In this article we show that it is possible to use non-linear reconstructors to extend the dynamic range of one of the most sensitive wavefront sensors far beyond the designed limits: the Zernike wavefront sensor (ZWFS). Methods: We trained a convolutional neural network (CNN) completely in simulation to perform the phase reconstruction of a vector-ZWFS (v-ZWFS), a higher dynamic-range variant of the ZWFS. Contrary to the linear method, the CNN uses the information across the full frame to reconstruct the phase at each point, enabling it to resolve the ambiguities introduced by the periodic response of the ZWFS and thereby extend its effective capture range. We developed a two-step training strategy that ensured closed-loop stability and used a physically informed loss function to maximize the performance of the CNN. Results: We successfully closed the loop on-sky with the v-ZWFS using the CNN in observing conditions that the linear reconstructor could not converge to a stable flat wavefront. In cases where both reconstruction methods were working, the CNN outperformed the linear method in almost all cases, and in favorable seeing conditions we were even able to close the loop with the ZWFS acting as a first stage WFS, highlighting the extended dynamic range brought by the use of a non-linear wavefront reconstructor. Conclusions: We conclude that the use of non-linear wavefront reconstructor can extend the use cases of adaptive optics systems, especially when the WFS used shows highly non-linear behaviors.