November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Reconstruction of cosmological initial conditions with sequential simulation-based inference

Nov 28, 2023, 3:24 PM
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Flash talk Paris Contributed talks


Oleg Savchenko (University of Amsterdam)


Knowledge of the primordial matter density field from which the present non-linear observations formed is of fundamental importance for cosmology, as it contains an immense wealth of information about the physics, evolution, and initial conditions of the universe. Reconstructing this density field from the galaxy survey data is a notoriously difficult task, requiring sophisticated statistical methods, advanced cosmological simulators, and exploration of a multi-million-dimensional parameter space. In this talk, I will discuss how Gaussian Autoregressive Neural Ratio Estimation (a recent approach in simulation-based inference) allows us to tackle this problem and sequentially obtain data-constrained realisations of the primordial dark matter density field in a simulation-efficient way for general non-differentiable simulators. In addition, I will describe how graph neural networks can be used to get optimal data summaries for galaxy maps, and how our results compare to those obtained with classical likelihood-based methods such as Hamiltonian Monte Carlo.

Primary author

Oleg Savchenko (University of Amsterdam)

Presentation materials