Description
Chair: François Lanusse
The complexity of astrophysical data and the presence of unknowable systematics pose significant challenges to robustly extracting information about fundamental physics using conventional methods. I will describe how overcoming these challenges will require a qualitative shift in our approach to statistical inference, bringing together several recent advances in generative modeling, differentiable programming, and simulation-based inference. As case studies, I will show examples of using simulation-based inference to extract the dark matter content from dwarf galaxies, and the use of diffusion-based generative modeling to encode the likelihood of galaxy clustering statistics.
