BibTeX

@article{2502.03139v1,
Author        = {Oleg Savchenko and Guillermo Franco Abellán and Florian List and Noemi Anau Montel and Christoph Weniger},
Title         = {Fast Sampling of Cosmological Initial Conditions with Gaussian Neural
  Posterior Estimation},
Eprint        = {2502.03139v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Knowledge of the primordial matter density field from which the large-scale
structure of the Universe emerged over cosmic time is of fundamental importance
for cosmology. However, reconstructing these cosmological initial conditions
from late-time observations is a notoriously difficult task, which requires
advanced cosmological simulators and sophisticated statistical methods to
explore a multi-million-dimensional parameter space. We show how
simulation-based inference (SBI) can be used to tackle this problem and to
obtain data-constrained realisations of the primordial dark matter density
field in a simulation-efficient way with general non-differentiable simulators.
Our method is applicable to full high-resolution dark matter $N$-body
simulations and is based on modelling the posterior distribution of the
constrained initial conditions to be Gaussian with a diagonal covariance matrix
in Fourier space. As a result, we can generate thousands of posterior samples
within seconds on a single GPU, orders of magnitude faster than existing
methods, paving the way for sequential SBI for cosmological fields.
Furthermore, we perform an analytical fit of the estimated dependence of the
covariance on the wavenumber, effectively transforming any point-estimator of
initial conditions into a fast sampler. We test the validity of our obtained
samples by comparing them to the true values with summary statistics and
performing a Bayesian consistency test.},
Year          = {2025},
Month         = {Feb},
Url           = {http://arxiv.org/abs/2502.03139v1},
File          = {2502.03139v1.pdf}
}