On-sky validation of image-based adaptive optics wavefront sensor referencing

Abstract

Differentiating between an exoplanet signal and residual speckle noise is a key challenge in high-contrast imaging. Speckles are due to a combination of fast, slow and static wavefront aberrations introduced by atmospheric turbulence and instrument optics. While wavefront control techniques developed over the last decade have shown promise in minimizing fast atmospheric residuals, slow and static aberrations such as non-common path aberrations (NCPAs) remain a key limiting factor for exoplanet detection. NCPA are not seen by the wavefront sensor (WFS) of the adaptive optics (AO) loop, hence the difficulty in correcting them. We propose to improve the identification and rejection of those aberrations. The algorithm DrWHO, performs frequent compensation of static and quasi-static aberrations to boost image contrast. By changing the WFS reference at every iteration of the algorithm, DrWHO changes the AO point of convergence to lead it towards a compensation of the static and slow aberrations. References are calculated using an iterative lucky-imaging approach, where each iteration updates the WFS reference, ultimately favoring high-quality focal plane images. We validate this concept through numerical simulations and on-sky testing on the SCExAO instrument at the 8.2-m Subaru telescope. Simulations show a rapid convergence towards the correction of 82% of the NCPAs. On-sky tests are performed over a 10-minute run in the visible (750 nm). We introduce a flux concentration (FC) metric to quantify the point spread function (PSF) quality and measure a 15.7% improvement. The DrWHO algorithm is a robust focal-plane wavefront sensing calibration method that has been successfully demonstrated on sky. It does not rely on a model nor requires wavefront sensor calibration or linearity. It is compatible with different wavefront control methods, and can be further optimized for speed and efficiency.