BibTeX

@article{2507.03086v1,
Author        = {Sébastien Pierre and Bruno Régaldo-Saint Blancard and ChangHoon Hahn and Michael Eickenberg},
Title         = {Mitigating Model Misspecification in Simulation-Based Inference for
  Galaxy Clustering},
Eprint        = {2507.03086v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Simulation-based inference (SBI) has become an important tool in cosmology
for extracting additional information from observational data using
simulations. However, all cosmological simulations are approximations of the
actual universe, and SBI methods can be sensitive to model misspecification -
particularly when the observational data lie outside the support of the
training distribution. We present a method to improve the robustness of
cosmological analyses under such conditions. Our approach first identifies and
discards components of the summary statistics that exhibit inconsistency across
related simulators, then learns a transformation that brings the observation
back within the support of the training distribution. We apply our method in
the context of a recent SimBIG SBI galaxy clustering analysis using the wavelet
scattering transform (WST) summary statistic. The original analysis struggled
to produce robust constraints for certain subsets of WST coefficients, where
the observational data appeared out-of-distribution (OOD) relative to the
training data. We show that our method enables robust cosmological inference
and resolves OOD issues, while preserving most of the constraining power. In
particular, the improved SimBIG WST analysis yields $\Lambda$CDM constraints of
$\Omega_m = 0.32^{+0.02}_{-0.02}$ and $\sigma_8 = 0.80^{+0.02}_{-0.02}$, which
are respectively $1.4\times$ and $3.1\times$ tighter than those from a standard
perturbation-theory-based power spectrum analysis, confirming the significant
information gain of WST summary statistics. The proposed method is easily
applicable to other cosmological SBI contexts and represents a step toward more
robust SBI pipelines.},
Year          = {2025},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2507.03086v1},
File          = {2507.03086v1.pdf}
}