Oral Presentation

Field-Level BAO Reconstruction and BAOnd

Author(s): Adrian Bayer (Flatiron Institute/Princeton University)

Presenter: Adrian Bayer (Flatiron Institute / Princeton University)

I will motivate field-level inference as an optimal approach to extracting information from cosmic structure and reconstructing the initial conditions of the Universe. In particular, I will review different methods of field-level inference, ranging from differentiable forward modeling to machine learning approaches, and highlight the potential for improving BAO constraints with DESI. Time permitting, I will also discuss and interpret which parts of the cosmic web neural networks pay most attention to during field-level inference, and explore the robustness of cosmological N-body simulations at the field level.