Bayesian Simulation-based Inference for Cosmological Initial Conditions. (arXiv:2310.19910v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+List_F/0/1/0/all/0/1">Florian List</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Montel_N/0/1/0/all/0/1">Noemi Anau Montel</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Weniger_C/0/1/0/all/0/1">Christoph Weniger</a>
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.
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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