Shiro Ikeda

Recovering Missing Degrees of Freedom in Simulation-Trained Field-Level Generative Models

Simulation-trained generative models are emerging as a powerful framework for field-level inference in cosmology, particularly in strongly nonlinear regimes where analytical approaches become intractable. However, the finite size of training datasets often leads to variance shrinkage, indicating that part of the realiz...