BibTeX

@article{2505.00632v2,
Author        = {Kangning Diao and Biwei Dai and Uros Seljak},
Title         = {Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing
  Maps},
Eprint        = {2505.00632v2},
DOI           = {10.1088/1475-7516/2025/08/004},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {Simulation-based inference provides a powerful framework for extracting rich
information from nonlinear scales in current and upcoming cosmological surveys,
and ensuring its robustness requires stringent validation of forward models. In
this work, we recast forward model validation as an out-of-distribution (OoD)
detection problem within the framework of machine learning (ML)-based
simulation-based inference (SBI). We employ probability density as the metric
for OoD detection, and compare various density estimation techniques,
demonstrating that field-level probability density estimation via continuous
time flow models (CTFM) significantly outperforms feature-level approaches that
combine scattering transform (ST) or convolutional neural networks (CNN) with
normalizing flows (NFs), as well as NF-based field-level estimators, as
quantified by the area under the receiver operating characteristic curve
(AUROC). Our analysis shows that CTFM not only excels in detecting OoD samples
but also provides a robust metric for model selection. Additionally, we
verified CTFM maintains consistent efficacy across different cosmologies while
mitigating the inductive biases inherent in NF architectures. Although our
proof-of-concept study employs simplified forward modeling and noise settings,
our framework establishes a promising pathway for identifying unknown
systematics in the cosmology datasets.},
Year          = {2025},
Month         = {May},
Note          = {JCAP08(2025)004},
Url           = {http://arxiv.org/abs/2505.00632v2},
File          = {2505.00632v2.pdf}
}