Hybrid deep learning-based phase diversity method for wavefront reconstruction

Abstract

The efficiency of high-power laser systems is limited by wavefront distortions in the beam, particularly non-common path aberrations, which reduce the peak intensity at the focal plane. Compensating for these aberrations requires the calibration of the adaptive optics system. Conventional calibration methods rely on a time-consuming iterative optimization that is highly sensitive to initial conditions. While deep learning-based models offer high speed, they often demonstrate insufficient accuracy. In this work, we present a hybrid wavefront reconstruction method that combines a convolutional neural network to generate an initial estimate of the wavefront distortions, with the L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm for its subsequent refinement. In numerical simulations, the method achieved an efficiency of $\sim 0.99$ in 80% of the cases for a root-mean-square (RMS) of wavefront distortions ranging from 0 to $1.3\hat{I}»$. In a physical experiment, for initial wavefront distortions with RMS values from 0.15 to $0.6\hat{I}»$, the method achieved an efficiency of $\sim 0.75$. As a result, focusing with a Strehl ratio of $0.96\pm 0.02$ was attained within 2 to 4 iterations of the algorithm, confirming the applicability of the method for the fast and accurate calibration of adaptive optics systems under real experimental conditions.

13 pages, 10 figures. The following article has been submitted to Review of Scientific Instruments. After it is published, it will be found at https://pubs.aip.org/aip/rsi