Date

2025-Jan-30

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