Neural networks and PCA coefficients to identify and correct aberrations in adaptive optics

Context. Static and quasi-static aberrations represent a great limit for high-contrast imaging in large telescopes. Among them the most important ones are all the aberrations not corrected by the adaptive optics (AO) system, which are called non-common path aberrations (NCPA). Several techniques have been proposed to mitigate it. The typical approach is to set an offset on the AO system with exactly the opposite sign of the NCPA in order to correct for the aberrations introduced by all the optical components downstream the wave-front sensor (WFS) up to the science camera. An estimate of the NCPA can be obtained with a trial-and-error approach or by more sophisticated techniques of focal-plane wave-front sensing. Aims. In all cases, a fast procedure is desirable to limit the telescope downtime and to repeat, if needed, the correction procedure to cope with the temporal variation of the NCPA. Very recently, new approaches based on neural networks (NNs) have also been proposed as an alternative. Methods. In this work, through simulated images, we test the application of a supervised NN for the mitigation of NCPAs in high-contrast imaging at visible wavelengths and, in particular, we investigate the possibility of applying this method to fast imagers such as SHARK-VIS, the forthcoming visible-band high-contrast imager for the Large Binocular Telescope (LBT). Results. Preliminary results show a measurement accuracy of the NCPA of 2 nm root mean square (RMS) for each sensed Zernike mode in turbulence-free conditions, and 5 nm RMS per mode when the residual turbulence has a wave-front error (WFE) of approximately 42.5 nm RMS, a typical value during LBT AO system calibration. This measurement is sufficient to guarantee that, after correction, NCPA residuals in the system are negligible compared to the typical WFE > 100 nm RMS of the best AO systems at large telescopes. Conclusions. Our simulations show this method is robust even in the presence of turbulence-induced aberrations that are not labelled in the training phase of the NN. The method could thus be used in a real-world setting by offloading a corrective static offset to the AO system of a telescope to mitigate the NCPA.