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The self-similar evolution of stationary point processes via persistent homology

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arxiv 2012.05751 v3 pith:PPR6PTCC submitted 2020-12-10 math.PR

The self-similar evolution of stationary point processes via persistent homology

classification math.PR
keywords pointhomologypersistentprocessesscalingself-similarcloudclouds
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Persistent homology provides a robust methodology to infer topological structures from point cloud data. Here we explore the persistent homology of point clouds embedded into a probabilistic setting, exploiting the theory of point processes. We introduce measures on the space of persistence diagrams and the self-similar scaling of a one-parameter family of these. As the main result we prove a packing relation between the occurring scaling exponents.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Towards a topological data analysis for heavy-ion collisions

    nucl-th 2025-09 conditional novelty 5.0

    Persistent homology Betti curves and persistence distributions for Trajectum Pb-Pb and O-O events are robust and reflect known flow and multiplicity phenomenology, with no enhanced parameter sensitivity over standard ...