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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.02636 |
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| _version_ | 1866912466641879040 |
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| author | Yip, Jacky H. T. Rouhiainen, Adam Shiu, Gary |
| author_facet | Yip, Jacky H. T. Rouhiainen, Adam Shiu, Gary |
| contents | The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from the tool remains an open question. To address this, we propose a neural network model to map persistence images to cosmological parameters. Through a parameter recovery test, we demonstrate that our model makes accurate and precise estimates, considerably outperforming conventional Bayesian inference approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_02636 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure Yip, Jacky H. T. Rouhiainen, Adam Shiu, Gary Cosmology and Nongalactic Astrophysics Machine Learning Algebraic Topology The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from the tool remains an open question. To address this, we propose a neural network model to map persistence images to cosmological parameters. Through a parameter recovery test, we demonstrate that our model makes accurate and precise estimates, considerably outperforming conventional Bayesian inference approaches. |
| title | Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure |
| topic | Cosmology and Nongalactic Astrophysics Machine Learning Algebraic Topology |
| url | https://arxiv.org/abs/2308.02636 |