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A statistical approach to knot confinement via persistent homology
Daniele Celoria1, Barbara I Mahler1
1Mathematical Institute, University of Oxford, Radcliffe Observatory, Andrew Wiles Building, Woodstock Rd, Oxford OX2 6GG, UK.
This study uses topological methods and persistent homology to analyze how random knots fill space. Findings reveal correlations between knot geometry and topological features, offering a way to measure knot deviations.
Area of Science:
- Topology
- Computational Geometry
- Data Analysis
Background:
- Understanding the spatial occupation of random knots is a fundamental problem in knot theory.
- Topological methods offer powerful tools for characterizing complex spatial structures.
Purpose of the Study:
- To investigate the volume occupied by randomly generated knots using topological approaches.
- To establish correlations between geometric properties and topological features of knot embeddings.
- To develop a metric for quantifying deviations of knots from ideal configurations.
Main Methods:
- Utilizing persistent homology (PH) on Vietoris-Rips complexes derived from knot point clouds.
- Analyzing the evolution of the first homology of metric neighborhoods for growing radii.
- Performing statistical analysis to identify correlations between geometric and PH-based features.
Main Results:
- Demonstrated increasing correlations between geometric quantities and PH features with increasing knot length.
- Observed variations in these correlations across different knot types.
- Established a framework for defining knot deviation from ideal configurations.
Conclusions:
- Persistent homology provides a robust method for quantifying spatial properties of knots.
- The study reveals a relationship between knot length, topology, and spatial occupation.
- The developed framework offers new insights into knot characterization and analysis.
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