BibTeX

@article{2411.08957v1,
Author        = {Kai Lehman and Sven Krippendorf and Jochen Weller and Klaus Dolag},
Title         = {Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs
  with SBI},
Eprint        = {2411.08957v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {How much cosmological information can we reliably extract from existing and
upcoming large-scale structure observations? Many summary statistics fall short
in describing the non-Gaussian nature of the late-time Universe in comparison
to existing and upcoming measurements. In this article we demonstrate that we
can identify optimal summary statistics and that we can link them with existing
summary statistics. Using simulation based inference (SBI) with automatic
data-compression, we learn summary statistics for galaxy catalogs in the
context of cosmological parameter estimation. By construction these summary
statistics do not require the ability to write down an explicit likelihood. We
demonstrate that they can be used for efficient parameter inference. These
summary statistics offer a new avenue for analyzing different simulation models
for baryonic physics with respect to their relevance for the resulting
cosmological features. The learned summary statistics are low-dimensional,
feature the underlying simulation parameters, and are similar across different
network architectures. To link our models, we identify the relevant scales
associated to our summary statistics (e.g. in the range of modes between $k= 5
- 30 h/\mathrm{Mpc}$) and we are able to match the summary statistics to
underlying simulation parameters across various simulation models.},
Year          = {2024},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2411.08957v1},
File          = {2411.08957v1.pdf}
}