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.
@article{arxiv.2509.13435,
title = {Field-level inference in cosmology},
author = {Florent Leclercq},
journal= {arXiv preprint arXiv:2509.13435},
year = {2025}
}
Comments
Lectures given at the Les Houches summer school on the Dark Universe (7 July - 1 August 2025). 24 pages, 5 figures. Submitted to SciPost Physics Lecture Notes