Abstract: Improving cosmological constraints from galaxy clustering presents several challenges, particularly in extracting information beyond the power spectrum due to the complexities involved in higher-order n-point function analyses. I will introduce novel inference techniques that allow us to go beyond state-of-the-art analyses, not only by utilizing the galaxy trispectrum, a task that remains computationally infeasible with traditional methods, but also by accessing the full information encoded in the galaxy density field for the first time in cosmological analysis. This is achieved using LEFTfield, a Lagrangian forward model based on the Effective Field Theory of Large Scale Structure (EFTofLSS) and the bias expansion, ensuring robustness on large scales. I will show how to obtain the posterior of cosmological, bias and noise parameters from measured summary statistics in LEFTfield through simulation-based inference (SBI), a deep learning technique that bypasses the need for explicit analytical likelihoods or covariance matrices. Additionally, as LEFTfield enables field-level Bayesian inference (FBI), I will conclude with a comparison of σ8 constraints from the full 3D galaxy density field using FBI against those obtained from n-point functions using SBI, assessing the question of how much cosmological information can be extracted at the field level.

Zoom info: https://stanford.zoom.us/my/sihanyuan?pwd=QnpsUHZWWGJ2ekVYWmZVL3BmM0gzZz09