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

Abstract

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, Ω_m 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.