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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Bayesian Simulation-based Inference for Cosmological Initial Conditions

Noemi Anau Montel · Florian List · Christoph Weniger


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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