Challenges and Prospects in Forward Modeling for Field-level Inference

Explore the challenges and prospects in forward modeling for field-level inference in this 42-minute conference talk by Beatriz Tucci from Stanford University. Delve into advanced computational methods used to analyze large-scale structure data and cosmic microwave background observations in modern cosmological surveys. Learn about the technical difficulties encountered when implementing forward modeling approaches for extracting cosmological parameters from field-level data, including issues with computational complexity, systematic uncertainties, and model validation. Discover the promising developments and future opportunities in this rapidly evolving field, particularly in the context of next-generation surveys that will provide unprecedented data quality and volume. Examine how forward modeling techniques can enhance our understanding of dark matter, dark energy, and the fundamental physics governing cosmic structure formation. Gain insights into the mathematical frameworks and statistical methods that enable researchers to connect theoretical predictions with observational data at the field level, moving beyond traditional summary statistics to extract maximum information from cosmological datasets.