Reconstructing astrophysical and cosmological fields from observations is challenging. It requires accounting for non-linear transformations, mixing of spatial structure, and noise. In contrast, forward simulators that map fields to observations are readily available for many applications. We present a versatile Bayesian field reconstruction algorithm rooted in simulation-based inference and enhanced by autoregressive modeling. The proposed technique is applicable to generic (non-differentiable) forward simulators and allows sampling from the posterior for the underlying field. We show first promising results on a proof-of-concept application: the recovery of cosmological initial conditions from late-time density fields.
@article{arxiv.2310.19910,
title = {Bayesian Simulation-based Inference for Cosmological Initial Conditions},
author = {Florian List and Noemi Anau Montel and Christoph Weniger},
journal= {arXiv preprint arXiv:2310.19910},
year = {2023}
}
Comments
Accepted for the NeurIPS 2023 workshop Machine Learning and the Physical Sciences; 5 pages, 1 figure