High-dimensional Bayesian Astronomical Imaging and Inverse Problems with Machine Learning
Abstract:
Recent progress in machine learning and generative modeling has opened new avenues to tackle previously insoluble high-dimensional inverse problems in astronomy and astrophysics, particularly in Bayesian image reconstruction. While these methodologies show great promise in a range of applications from field-level cosmology to differentiable optics systems, multiple open problems stand in the way of groundbreaking discoveries. This session will explore recent applications and proposals addressing the development of computationally tractable methodologies to reconstruct posterior samples in imaging problems. We will also discuss assessing their accuracy in real-world settings and addressing the problem of robustness to distributional shifts, which remains a key focus of current research.
Organiser & chair: Laurence Perreault Levasseur
Session Schedule
- 17:30 - 17:50
- Invited talk
- Laurence Perreault-Levasseur
- 17:50 - 18:10
- Invited talk
- François Lanusse
- 18:10 - 18:30
- Invited talk
- Peter Melchior
- 18:30 - 18:50
- Invited talk
- Carolina Cuesta-Lazarro
- 18:50 - 19:10
- Invited talk
- Peter Tuthill
- 19:10 - 20:30
- Invited poster
- Noemi Montel
- 19:10 - 20:30
- Invited poster
- Alexandre Adam
- 19:10 - 20:30
- Contributed poster
- TBC
- 19:10 - 20:30
- Contributed poster
- TBC
- 19:10 - 20:30
- Contributed poster
- TBC