BIALEK, Spencer; BERTIN, Emmanuel; FABBRO, Sébastien; BOUY, Hervé; RIVET, Jean-Pierre; LAI, Olivier; CUILLANDRE, Jean-Charles

doi:10.1093/mnras/stae1018

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BIALEK, Spencer
University of Victoria [Canada] [UVIC]

BERTIN, Emmanuel
Institut Polytechnique de Paris [IP Paris]
Département Réseaux et Services Multimédia Mobiles [TSP - RS2M]
Network Systems and Services [NeSS-SAMOVAR]
Orange Innovation

FABBRO, Sébastien
NRC Herzberg Astronomy and Astrophysics

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Monthly Notices of the Royal Astronomical Society. 2024-04-19, vol. 531, n° 1, p. 403-421

Oxford University Press (OUP): Policy P - Oxford Open Option A

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ABSTRACT We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure (∼0.2 s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image.< Réduire

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Data analysis

Observational

Image processing

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Simulations

Turbulence

Atmospheric effects

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DanceCam: atmospheric turbulence mitigation in wide-field astronomical images with short-exposure video streams

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