A Deep Reinforcement Learning Approach to Wavefront Control for Exoplanet Imaging

TL;DR

This paper introduces a model-free deep reinforcement learning method to improve wavefront control in exoplanet imaging, aiming to create dark holes more efficiently and accurately than traditional techniques.

Contribution

It develops a novel data-driven reinforcement learning approach for wavefront control, reducing reliance on physical models and iterative procedures.

Findings

Successful aberration correction in simulations

Promising dark hole creation results

Potential for real-world application

Abstract

Exoplanet imaging uses coronagraphs to block out the bright light from a star, allowing astronomers to observe the much fainter light from planets orbiting the star. However, these instruments are heavily impacted by small wavefront aberrations and require the minimization of starlight residuals directly in the focal plane. State-of-the art wavefront control methods suffer from errors in the underlying physical models, and often require several iterations to minimize the intensity in the dark hole, limiting performance and reducing effective observation time. This study aims at developing a data-driven method to create a dark hole in post-coronagraphic images. For this purpose, we leverage the model-free capabilities of reinforcement learning to train an agent to learn a control strategy directly from phase diversity images acquired around the focal plane. Initial findings demonstrate successful aberration correction in non-coronagraphic simulations and promising results for dark hole creation in post-coronagraphic scenarios. These results highlight the potential of model-free reinforcement learning for dark-hole creation, justifying further investigation and eventually experimental validation on a dedicated testbed.