Mean-Field Simulation-Based Inference for Cosmological Initial Conditions

Autor(en)

Oleg Savchenko, Florian List, Guillermo Franco Abellán, Noemi Anau Montel, Christoph Weniger

Abstrakt

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.

Organisation(en)

Institut für Astrophysik

Externe Organisation(en)

University of Amsterdam (UvA)

Anzahl der Seiten

9

Publikationsdatum

10-2024

Peer-reviewed

Ja

ÖFOS 2012

103004 Astrophysik, 102019 Machine Learning

Schlagwörter

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/9d558fd0-3784-443a-bcc2-9c5289d8fe37