Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
This addresses the challenge of detailed galaxy structure analysis for astronomers, though it appears incremental as it builds on existing score-based and diffusion model techniques.
The paper tackles the problem of analyzing faint galaxy structures in noisy, blurred astronomical images by introducing a Bayesian framework that combines score-based likelihood characterization and diffusion model priors for image deconvolution. The result is that the method recovers structures from Hubble Space Telescope data that were previously only visible in next-generation James Webb Space Telescope imaging.
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.