A single unconditioned diffusion model plus a rescaled Diffusion Posterior Sampling step reconstructs weak lensing mass maps whose power spectra and non-Gaussian statistics match the simulations.
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
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abstract
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.
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Diffusion-based mass map reconstruction from weak lensing data
A single unconditioned diffusion model plus a rescaled Diffusion Posterior Sampling step reconstructs weak lensing mass maps whose power spectra and non-Gaussian statistics match the simulations.