BibTeX

@article{2509.03165v1,
Author        = {Anirban Bairagi and Benjamin Wandelt},
Title         = {PatchNet: A hierarchical approach for neural field-level inference from
  Quijote Simulations},
Eprint        = {2509.03165v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {\textit{What is the cosmological information content of a cubic Gigaparsec of
dark matter? } Extracting cosmological information from the non-linear matter
distribution has high potential to tighten parameter constraints in the era of
next-generation surveys such as Euclid, DESI, and the Vera Rubin Observatory.
Traditional approaches relying on summary statistics like the power spectrum
and bispectrum, though analytically tractable, fail to capture the full
non-Gaussian and non-linear structure of the density field. Simulation-Based
Inference (SBI) provides a powerful alternative by learning directly from
forward-modeled simulations. In this work, we apply SBI to the \textit{Quijote}
dark matter simulations and introduce a hierarchical method that integrates
small-scale information from field sub-volumes or \textit{patches} with
large-scale statistics such as power spectrum and bispectrum. This hybrid
strategy is efficient both computationally and in terms of the amount of
training data required. It overcomes the memory limitations associated with
full-field training. We show that our approach enhances Fisher information
relative to analytical summaries and matches that of a very different approach
(wavelet-based statistics), providing evidence that we are estimating the full
information content of the dark matter density field at the resolution of $\sim
7.8~\mathrm{Mpc}/h$.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.03165v1},
File          = {2509.03165v1.pdf}
}