A deep reinforcement learning approach to wavefront control for exoplanet imaging

Yann Gutierrez; Johan Mazoyer; Olivier Herscovici-Schiller; Laurent M. Mugnier; Baptiste Abeloos; Iva Laginja

doi:10.1117/12.3020374

23 August 2024 A deep reinforcement learning approach to wavefront control for exoplanet imaging

Yann Gutierrez, Johan Mazoyer, Olivier Herscovici-Schiller, Laurent M. Mugnier, Baptiste Abeloos, Iva Laginja

Proceedings Volume 13092, Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave; 130926H (2024) https://doi.org/10.1117/12.3020374
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

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 aberrations in the wavefront and require the minimization of starlight residuals directly in the focal plane. Stateof-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.

Citation Download Citation

Yann Gutierrez, Johan Mazoyer, Olivier Herscovici-Schiller, Laurent M. Mugnier, Baptiste Abeloos, and Iva Laginja "A deep reinforcement learning approach to wavefront control for exoplanet imaging", Proc. SPIE 13092, Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave, 130926H (23 August 2024); https://doi.org/10.1117/12.3020374

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KEYWORDS

Machine learning

Wavefronts

Coronagraphy

Wavefront aberrations

Exoplanets

Signal to noise ratio

Aberration correction

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