Mean-Field Simulation-Based Inference for Cosmological Initial Conditions
- Author(s)
- Oleg Savchenko, Florian List, Guillermo Franco Abellán, Noemi Anau Montel, Christoph Weniger
- 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: ∼ 1 h on a GPU, sampling: ≲ 3 s for 1000 samples at resolution 1283),
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.- Organisation(s)
- Department of Astrophysics
- External organisation(s)
- University of Amsterdam (UvA)
- No. of pages
- 9
- Publication date
- 10-2024
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103004 Astrophysics, 102019 Machine learning
- Keywords
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/9d558fd0-3784-443a-bcc2-9c5289d8fe37