Fast Gaussian Marginal Neural Posterior Estimation of Cosmological Initial Conditions

Clicks: 1
ID: 314739
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #521 of 897 articles by views in monthly notices of the royal astronomical society

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 897 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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 with a Gaussian ansatz whose covariance matrix is diagonal in Fourier space. As a result, we can generate thousands of samples from this approximate posterior within seconds on a single GPU, orders of magnitude faster than existing methods, paving the way for active-learning SBI for cosmological fields. Furthermore, we parameterise the likelihood precision as an isotropic function of wavenumber k, specified by a small set of learned nodes, effectively transforming any point-estimator of initial conditions into a fast sampler. We also study how the learned precision varies with the observational noise level and the size of the training set. We test the validity of our obtained samples by comparing them to the true values with summary statistics and detailed posterior predictive tests, thus determining the range of validity of the Gaussian approximation.
Reference Key
openalex_W7162209391 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Олег Савченко, Guillermo Franco Abellán, Florian List, Noemi Anau Montel, Christoph Weniger
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag977
URL
https://doi.org/10.1093/mnras/stag977
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.