Cosmological Analysis

Cosmological analysis aims to constrain cosmological parameters and test models of the universe using observational data. Current research heavily utilizes machine learning, employing techniques like neural networks (including Bayesian neural networks and graph neural networks), normalizing flows, and diffusion models to efficiently emulate complex cosmological simulations and perform likelihood-free inference directly from high-dimensional data like galaxy distributions or cosmic microwave background maps. These advancements enable faster and more robust parameter estimation and model comparison, overcoming limitations of traditional methods based on summary statistics. This improved efficiency and accuracy is crucial for analyzing the massive datasets from current and upcoming cosmological surveys, leading to more precise measurements of fundamental cosmological parameters and a deeper understanding of the universe's evolution.