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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.01988 |
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| _version_ | 1866929201954684928 |
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| author | Van Huffel, Michael Etienne Barberi, Leonardo Aldo Alejandro Sagis, Tobias |
| author_facet | Van Huffel, Michael Etienne Barberi, Leonardo Aldo Alejandro Sagis, Tobias |
| contents | In this research, we investigate the structural evolution of the cosmic web, employing advanced methodologies from Topological Data Analysis. Our approach involves leveraging LITE, an innovative method from recent literature that embeds persistence diagrams into elements of vector spaces. Utilizing this methodology, we analyze three quintessential cosmic structures: clusters, filaments, and voids. A central discovery is the correlation between \textit{Persistence Energy} and redshift values, linking persistent homology with cosmic evolution and providing insights into the dynamics of cosmic structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01988 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Clustering in $Λ$CDM Cosmologies via Persistence Energy Van Huffel, Michael Etienne Barberi, Leonardo Aldo Alejandro Sagis, Tobias Cosmology and Nongalactic Astrophysics Computational Geometry Algebraic Topology Machine Learning In this research, we investigate the structural evolution of the cosmic web, employing advanced methodologies from Topological Data Analysis. Our approach involves leveraging LITE, an innovative method from recent literature that embeds persistence diagrams into elements of vector spaces. Utilizing this methodology, we analyze three quintessential cosmic structures: clusters, filaments, and voids. A central discovery is the correlation between \textit{Persistence Energy} and redshift values, linking persistent homology with cosmic evolution and providing insights into the dynamics of cosmic structures. |
| title | Hierarchical Clustering in $Λ$CDM Cosmologies via Persistence Energy |
| topic | Cosmology and Nongalactic Astrophysics Computational Geometry Algebraic Topology Machine Learning |
| url | https://arxiv.org/abs/2401.01988 |