Hierarchical Clustering in LambdaCDM Cosmologies via Persistence Energy
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Abstract (arXiv)
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 textitPersistence Energy and redshift values, linking persistent homology with cosmic evolution and providing insights into the dynamics of cosmic structures.
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References · 6
- 2018The persistence landscape and some of its propertiescited by 18
- 2019Understanding the Topology and the Geometry of the Space of Persistence Diagrams via Optimal Partial Transportcited by 17
- 2021Estimation and Quantization of Expected Persistence Diagramscited by 8
- 2022Approximating Persistent Homology for Large Datasetscited by 8
- 2023Statistical learning on measures: an application to persistence diagramscited by 2
- 2023Discrete transforms of quantized persistence diagramscited by 1
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