WI Brown Bag Seminar with Adrian Bayer

Abstract:

Modern cosmological surveys are entering a multi-probe era, combining galaxy clustering, weak lensing, and CMB measurements to constrain inflationary initial conditions, neutrino mass, the dark sector, and beyond. Extracting the full information content of these datasets requires inference methods that go beyond traditional summary statistics, while ensuring robustness and interpretability.

In this talk, I will motivate field-level inference, which directly fits the observed maps of the sky to reconstruct the initial conditions of the Universe and optimally infer cosmological parameters. I will review approaches ranging from differentiable forward modeling to simulation-based inference with neural networks, and highlight applications for baryon acoustic oscillations with DESI and for CMB science with the Simons Observatory. I will end by outlining a path to multi-probe analyses where independent observables are used to simultaneously increase constraining power and provide stringent internal consistency checks—establishing a robust, unified, end-to-end analysis of the entire cosmic sky to maximize the fundamental physics return of the next generation of cosmological surveys.