BibTeX

@article{2309.10270v1,
Author        = {Chirag Modi and Oliver H. E. Philcox},
Title         = {Hybrid SBI or How I Learned to Stop Worrying and Learn the Likelihood},
Eprint        = {2309.10270v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We propose a new framework for the analysis of current and future
cosmological surveys, which combines perturbative methods (PT) on large scales
with conditional simulation-based implicit inference (SBI) on small scales.
This enables modeling of a wide range of statistics across all scales using
only small-volume simulations, drastically reducing computational costs, and
avoids the assumption of an explicit small-scale likelihood. As a
proof-of-principle for this hybrid simulation-based inference (HySBI) approach,
we apply it to dark matter density fields and constrain cosmological parameters
using both the power spectrum and wavelet coefficients, finding promising
results that significantly outperform classical PT methods. We additionally lay
out a roadmap for the next steps necessary to implement HySBI on actual survey
data, including consideration of bias, systematics, and customized simulations.
Our approach provides a realistic way to scale SBI to future survey volumes,
avoiding prohibitive computational costs.},
Year          = {2023},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2309.10270v1},
File          = {2309.10270v1.pdf}
}