Bayesian Simulation-based Inference for Cosmological Initial Conditions
- Author(s)
- Florian List, Noemi Anau Montel, Christoph Weniger
- Abstract
Reconstructing astrophysical and cosmological fields from observations is challenging. It requires accounting for non-linear transformations, mixing of spatial structure, and noise. In contrast, forward simulators that map fields to observations are readily available for many applications. We present a versatile Bayesian field reconstruction algorithm rooted in simulation-based inference and enhanced by autoregressive modeling. The proposed technique is applicable to generic (non-differentiable) forward simulators and allows sampling from the posterior for the underlying field. We show first promising results on a proof-of-concept application: the recovery of cosmological initial conditions from late-time density fields.
- Organisation(s)
- Department of Astrophysics
- External organisation(s)
- University of Amsterdam (UvA)
- Journal
- Advances in neural information processing systems : ... proceedings of the ... conference
- No. of pages
- 9
- ISSN
- 1049-5258
- Publication date
- 10-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103004 Astrophysics, 103003 Astronomy, 102019 Machine learning
- Keywords
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/a7abab01-915c-4b32-a35f-94be94133ac8