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Mean-Field Simulation-Based Inference for Cosmological Initial Conditions

O Savchenko, F List, GF Abellán, NA Montel… - arXiv preprint arXiv …, 2024 - arxiv.org
Astrophysics paper astro-ph.CO Suggest

… 4]) and variational inference [52], we refer to our method as ‘mean-field simulation-based inference’. While our method, which focuses on the first two moments (mean and …

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BibTeX

@article{2410.15808v1,
Author        = {Oleg Savchenko and Florian List and Guillermo Franco Abellán and Noemi Anau Montel and Christoph Weniger},
Title         = {Mean-Field Simulation-Based Inference for Cosmological Initial
  Conditions},
Eprint        = {2410.15808v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Reconstructing cosmological initial conditions (ICs) from late-time
observations is a difficult task, which relies on the use of computationally
expensive simulators alongside sophisticated statistical methods to navigate
multi-million dimensional parameter spaces. We present a simple method for
Bayesian field reconstruction based on modeling the posterior distribution of
the initial matter density field to be diagonal Gaussian in Fourier space, with
its covariance and the mean estimator being the trainable parts of the
algorithm. Training and sampling are extremely fast (training: $\sim 1 \,
\mathrm{h}$ on a GPU, sampling: $\lesssim 3 \, \mathrm{s}$ for 1000 samples at
resolution $128^3$), and our method supports industry-standard
(non-differentiable) $N$-body simulators. We verify the fidelity of the
obtained IC samples in terms of summary statistics.},
Year          = {2024},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2410.15808v1},
File          = {2410.15808v1.pdf}
}

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