arXiv:2401.01988
공식 코드 · Papers with Code
Hierarchical Clustering in $Λ$CDM Cosmologies via Persistence Energy
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
HERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization
MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis
토론
이 논문과 연결된 포럼 글이 아직 없습니다.