BibTeX

@article{2504.15149v1,
Author        = {Chen Su and Huanyuan Shan and Cheng Zhao and Wenshuo Xu and Jiajun Zhang},
Title         = {Cosmological Constraints with Void Lensing I: the Simulation-Based
  Inference Framework},
Eprint        = {2504.15149v1},
DOI           = {10.1051/0004-6361/202555237},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present a Simulation-Based Inference (SBI) framework for cosmological
parameter estimation via void lensing analysis. Despite the absence of an
analytical model of void lensing, SBI can effectively learn posterior
distributions through forward modeling of mock data. We develop a forward
modeling pipeline that accounts for both cosmology and the galaxy-halo
connection. By training a neural density estimator on simulated data, we infer
the posteriors of two cosmological parameters, $\Omega_m$ and $S_8$. Validation
tests are conducted on posteriors derived from different cosmological
parameters and a fiducial sample. The results demonstrate that SBI provides
unbiased estimates of mean values and accurate uncertainties. These findings
highlight the potential to apply void lensing analysis to observational data
even without an analytical void lensing model.},
Year          = {2025},
Month         = {Apr},
Note          = {A&A 699, A174 (2025)},
Url           = {http://arxiv.org/abs/2504.15149v1},
File          = {2504.15149v1.pdf}
}