Bayesian Simulation-based Inference for Cosmological Initial Conditions

• URL: http://arxiv.org/abs/2310.19910v1
• Date: Mon, 30 Oct 2023 18:24:25 GMT
• Title: Bayesian Simulation-based Inference for Cosmological Initial Conditions
• Authors: Florian List, Noemi Anau Montel, Christoph Weniger
• Abstract summary: We present a versatile Bayesian field reconstruction algorithm rooted in simulation-based inference and enhanced by autoregressive modeling. We show first promising results on a proof-of-concept application: the recovery of cosmological initial conditions from late-time density fields.
• Score: 5.954511401622426
• License: http://creativecommons.org/licenses/by/4.0/
• 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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