Iremsu Taskin | Searcharxiv

arXiv2026

Exploring reinforcement learning to enhance focal-plane wavefront control for vortex coronagraphs

High Contrast Imaging (HCI) on ground-based telescopes suffers from phase aberrations on the observed wavefront caused by atmospheric turbulence. Adaptive Optics (AO) systems are adept at correcting these aberrations, but fall short in the correction of non-common path aberrations (NCPAs). NCPAs arise because the wavefront sensor (WFS) measures and corrects a wavefront that is different from that affecting the science images, thus requiring additional intervention. This work makes use of focal-plane wavefront sensing and reinforcement learning (RL) to address the wavefront aberrations caused by NCPAs. The PO4NCPA algorithm utilizes sequential phase diversity to address phase ambiguities and is tested on a simulation designed for the Mid-infrared ELT Imager and Spectrograph (METIS) instrument. In this paper, we present the performance of PO4NCPA with scalar and vector vortex coronagraphs to demonstrate its flexibility.

astro-ph.IM↗

arXiv2026

Focal plane wavefront control with model-based reinforcement learning

The direct imaging of potentially habitable exoplanets is one prime science case for high-contrast imaging instruments on extremely large telescopes. Most such exoplanets orbit close to their host stars, where their observation is limited by fast-moving atmospheric speckles and quasi-static non-common-path aberrations (NCPA). Conventional NCPA correction methods often use mechanical mirror probes, which compromise performance during operation. This work presents machine-learning-based NCPA control methods that automatically detect and correct both dynamic and static NCPA errors by leveraging sequential phase diversity. We extend previous work in reinforcement learning for AO to focal plane control. A new model-based RL algorithm, Policy Optimization for NCPAs (PO4NCPA), interprets the focal-plane image as input data and, through sequential phase diversity, determines phase corrections that optimize both non-coronagraphic and post-coronagraphic PSFs without prior system knowledge. Further, we demonstrate the effectiveness of this approach by numerically simulating static NCPA errors on a ground-based telescope and an infrared imager affected by water-vapor-induced seeing (dynamic NCPAs). Simulations show that PO4NCPA robustly compensates static and dynamic NCPAs. In static cases, it achieves near-optimal focal-plane light suppression with a coronagraph and near-optimal Strehl without one. With dynamics NCPA, it matches the performance of the modal least-squares reconstruction combined with a 1-step delay integrator in these metrics. The method remains effective for the ELT pupil, vector vortex coronagraph, and under photon and background noise. PO4NCPA is model-free and can be directly applied to standard imaging as well as to any coronagraph. Its sub-millisecond inference times and performance also make it suitable for real-time low-order correction of atmospheric turbulence beyond HCI.

astro-ph.IM↗