Bayesian Simulation-based Inference for Cosmological Initial Conditions

Florian List; Noemi Anau Montel; Christoph Weniger

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arXiv · 2310.19910

Bayesian Simulation-based Inference for Cosmological Initial Conditions

Florian List·Noemi Anau Montel·Christoph Weniger

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Abstract

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.

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Florian List, Noemi Anau Montel, Christoph Weniger. 2023-10-30. Bayesian Simulation-based Inference for Cosmological Initial Conditions. https://arxiv.org/abs/2310.19910

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