Mean-Field Simulation-Based Inference for Cosmological Initial Conditions

• URL: http://arxiv.org/abs/2410.15808v1
• Date: Mon, 21 Oct 2024 09:23:50 GMT
• Title: Mean-Field Simulation-Based Inference for Cosmological Initial Conditions
• Authors: Oleg Savchenko, Florian List, Guillermo Franco Abellán, Noemi Anau Montel, Christoph Weniger,
• Abstract summary: 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. Training and sampling are extremely fast (training: $sim 1, mathrmh$ on a GPU, sampling: $lesssim 3, mathrms$ for 1000 samples at resolution $1283$), and our method supports industry-standard (non-differentiable) $N$-body simulators.
• Score: 4.520518890664213
• License:
• 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.

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