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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15149 |
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| _version_ | 1866918085759336448 |
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| author | Su, Chen Shan, Huanyuan Zhao, Cheng Xu, Wenshuo Zhang, Jiajun |
| author_facet | Su, Chen Shan, Huanyuan Zhao, Cheng Xu, Wenshuo Zhang, Jiajun |
| contents | 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, $Ω_m$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cosmological Constraints with Void Lensing I: the Simulation-Based Inference Framework Su, Chen Shan, Huanyuan Zhao, Cheng Xu, Wenshuo Zhang, Jiajun Cosmology and Nongalactic Astrophysics 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, $Ω_m$ 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. |
| title | Cosmological Constraints with Void Lensing I: the Simulation-Based Inference Framework |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2504.15149 |