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

TL;DR

This paper presents a neural network-based technique that processes short-exposure astronomical videos to significantly reduce atmospheric turbulence effects, enhancing image resolution and stability.

Contribution

A novel neural network method trained on simulated data that effectively mitigates turbulence in wide-field astronomical images using short video sequences.

Findings

Improved angular resolution in astronomical images.

Effective disentangling of stellar sources across seeing conditions.

Preservation of flux with lower SNR than traditional stacking.

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 ($\sim$0.2s) 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.