Jean-Charles Cuillandre

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 (∼0.2s) 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.

We present astrophot, a fast, powerful, and user-friendly python based astronomical image photometry solver. astrophot incorporates automatic differentiation and graphics processing unit (GPU), or parallel central processing unit (CPU), acceleration, powered by the machine learning library pytorch. Everything: astrophot can fit models for sky, stars, galaxies, point spread functions (PSFs), and more in a principled χ2 forward optimization, recovering Bayesian posterior information and covariance of all parameters. Everywhere: astrophot can optimize forward models on CPU or GPU; across images that are large, multiband, multi-epoch, rotated, dithered, and more. All at once: The models are optimized together, thus handling overlapping objects and including the covariance between parameters (including PSF and galaxy parameters). A number of optimization algorithms are available including Levenberg–Marquardt, Gradient descent, and No-U-Turn Markov chain Monte Carlo sampling. With an object-oriented user interface, astrophot makes it easy to quickly extract detailed information from complex astronomical data for individual images or large survey programs. This paper outlines novel features of the astrophot code and compares it to other popular astronomical image modelling software. astrophot is open-source, fully python based, and freely accessible at https://github.com/Autostronomy/AstroPhot .