Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors
Abstract Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant barriers to such analysis are the nontrivial noise properties of real astronomical images and the point-spread function, 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 Hubble Space Telescope data, recovers structures which have otherwise only become visible in next-generation James Webb Space Telescope imaging.
Authors
- Alexandre Adam (ORCID: https://orcid.org/0000-0001-8806-7936)
- Laurence Perreault-Levasseur (ORCID: https://orcid.org/0000-0003-3544-3939)
- Connor Bottrell (ORCID: https://orcid.org/0000-0003-4758-4501)
- Ronan Legin (ORCID: https://orcid.org/0000-0001-9459-6316)
- Connor Stone (ORCID: https://orcid.org/0000-0002-9086-6398)
- Yashar Hezaveh (ORCID: https://orcid.org/0000-0002-8669-5733)
Institutions
- Kavli Institute for the Physics and Mathematics of the Universe (JP)
- Centre for Research in Astrophysics of Québec (CA)
- International Centre for Radio Astronomy Research (AU)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Université de Montréal (CA)
Publication Details
- Journal
- The Astronomical Journal
- Published
- 2025-04-08
- DOI
- https://doi.org/10.3847/1538-3881/adb039
- Citations
- 2
- Primary Topic
- Adaptive optics and wavefront sensing
- Type
- article
- Field-Weighted Citation Impact
- 2.49
Funders
- National Aeronautics and Space Administration
- Canadian Institute for Theoretical Astrophysics
- Space Telescope Science Institute
- Flatiron Health
- Canada Research Chairs
- Fonds de recherche du Québec
- Alliance de recherche numérique du Canada
- Natural Sciences and Engineering Research Council of Canada