Towards reconstructing the halo clustering and halo mass function of N-body simulations using neural ratio estimation

Understand

High-resolution cosmological N-body simulations are excellent tools for modelling the formation and clustering of dark matter haloes.

• These simulations suggest complex physical theories of halo formation governed by a set of effective physical parameters.
• Our goal is to extract these parameters and their uncertainties in a Bayesian context.
• We make a step towards automatising this process by directly comparing dark matter density projection maps extracted from cosmological simulations, with density projections generated from an analytical halo model.