SPRINT: System Parameters Recurrent INvasive Tracking, a fast and least-cost online calibration strategy for adaptive optics

TL;DR

This paper introduces SPRINT, a fast, low-cost online calibration method for adaptive optics systems that accurately tracks mis-registrations using a pseudo-synthetic model, ensuring optimal performance during scientific observations.

Contribution

It presents a novel invasive, model-based approach for real-time AO calibration that reduces complexity and maintains high accuracy with minimal impact on scientific data quality.

Findings

High accuracy in mis-registration parameter estimation

Robust performance under varying noise and turbulence conditions

Applicable to both Pyramid and Shack-Hartmann wavefront sensors

Abstract

The future large adaptive telescopes will trigger new constraints for the calibration of Adaptive Optics (AO) systems equipped with pre-focal Deformable Mirrors (DM). The image of the DM actuators grid as seen by the Wave-Front Sensor (WFS) may evolve during the operations due to the flexures of the opto-mechanical components present in the optical path. The latter will result in degraded AO performance that will impact the scientific operation. To overcome this challenge, it will be necessary to regularly monitor and compensate for these DM/WFS mis-registrations either by physically re-aligning some optical components or by updating the control matrix of the system. In this paper, we present a new strategy to track mis-registrations using a pseudo-synthetic model of the AO system. The method is based on an invasive approach where signals are acquired on-sky, before or during the scientific operations, and fed to the model to extract the mis-registration parameters. We introduce a method to compute the most sensitive modes to these mis-registrations that allows to reduce the number of degrees of freedom required by the algorithm and minimize the impact on the scientific performance. We demonstrate that, using only a few of these well selected signals, the method provides a very good accuracy on the parameters estimation, well under the targeted accuracy, and has a negligible impact on the scientific path. In addition, the method appears to be very robust to varying operating conditions of noise and atmospheric turbulence and performs equally for both Pyramid and Shack-Hartmann WFS.