A Deep Reinforcement Learning Approach to Wavefront Control for Exoplanet Imaging
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 dir...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
26.07.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2407.18733 |