Mean-Field Simulation-Based Inference for Cosmological Initial Conditions
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arXiv
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| Format: | Preprint |
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2024
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| author | Savchenko, Oleg List, Florian Abellán, Guillermo Franco Montel, Noemi Anau Weniger, Christoph |
| author_facet | Savchenko, Oleg List, Florian Abellán, Guillermo Franco Montel, Noemi Anau Weniger, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mean-Field Simulation-Based Inference for Cosmological Initial Conditions Savchenko, Oleg List, Florian Abellán, Guillermo Franco Montel, Noemi Anau Weniger, Christoph Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Mean-Field Simulation-Based Inference for Cosmological Initial Conditions |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2410.15808 |