Issue |
A&A
Volume 699, July 2025
|
|
---|---|---|
Article Number | A174 | |
Number of page(s) | 11 | |
Section | Cosmology (including clusters of galaxies) | |
DOI | https://doi.org/10.1051/0004-6361/202555237 | |
Published online | 07 July 2025 |
Cosmological constraints with void lensing
I. Simulation-based inference framework
1
Shanghai Astronomical Observatory (SHAO), Nandan Road 80, Shanghai 200030, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Department of Astronomy, Tsinghua University, Beijing 100084, China
⋆ Corresponding authors: suchen@shao.ac.cn; hyshan@shao.ac.cn; czhao@tsinghua.edu.cn
Received:
21
April
2025
Accepted:
23
May
2025
We present a simulation-based inference (SBI) framework for cosmological parameter estimation via a 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 developed a forward modeling pipeline that accounts for both the cosmology and the galaxy-halo connection. By training a neural density estimator (NDE) on simulated data, we were able to infer the posteriors of two cosmological parameters, Ωm and S8. Validation tests were 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 also highlight the potential for applying void-lensing analyses to observational data – even without an analytical void-lensing model.
Key words: cosmological parameters / dark matter / large-scale structure of Universe
© The Authors 2025
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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