Dynamic SBI for cosmological field-level inference

Abstract

Simulation-based inference (SBI) enables Bayesian analysis of complex cosmological data when only a forward model is available, while field-level inference (FLI) aims to perform inference in a maximally efficient way and retain more information than summary-statistic pipelines. In this talk, I will highlight recent advances and applications of SBI and FLI in cosmology. First, I will show how field-level SBI can be used to reconstruct cosmological fields from incomplete and noisy data. Using Gaussian neural posterior estimation with a trainable mean and covariance, and combining classical conjugate-gradient solvers with neural networks, our method captures complex spatial correlations, denoises observations, and probabilistically reconstructs missing regions. We demonstrate this approach on the challenging task of inferring the 3D dark matter field and its initial conditions. Finally, I will describe how this method can be embedded in an active-learning framework for dynamic SBI, enabling joint inference of fields and cosmological parameters by steering simulations toward the most relevant regions of parameter space.