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Explore advanced field-level inference methods for extracting optimal information from cosmic large-scale structure in this 17-minute conference lecture from the Erwin Schrödinger International Institute workshop on cosmic mapping theory and numerics. Learn about various field-level inference approaches including differentiable forward modeling and machine learning techniques, with particular emphasis on enhancing constraints from Baryon Acoustic Oscillation (BAO) reconstruction. Discover how neural networks analyze different components of the cosmic web during field-level inference processes, and examine the robustness of cosmological N-body simulations at the field level for reconstructing the Universe's initial conditions.
