Field-level inference in cosmology

Abstract

These lecture notes delve into field-level inference, a framework offering a robust way to extract more information and avoid biases compared to traditional methods for cosmological data analysis. The core idea is to analyse uncompressed maps to infer underlying physical fields and cosmological parameters. We introduce Bayesian hierarchical field-level models and discuss sampling techniques for exploring complex, high-dimensional posterior distributions. We review the framework that underpins field-level inference. Finally, we highlight some state-of-the-art applications across various cosmological probes, and the growing role of machine learning in enhancing field-level inference capabilities.