Applications of Persistent Homology in Nuclear Collisions

TL;DR

This paper introduces persistent homology as a new method to analyze nuclear collisions, enabling the extraction of topological features and clustering signatures from complex particle distribution data.

Contribution

It presents a novel application of persistent homology to nuclear physics, providing new observables for characterizing collision dynamics and correlations.

Findings

Identification of topological features in collision data

Detection of clustering signatures in particle distributions

Revealing long-range flow correlations

Abstract

We introduce a novel set of observables associated to the rapidly developing field of persistent homology for the quantitative characterization of nuclear collisions and their evolution. Persistent homology allows for the identification of topological and homological characteristics of distributions in multi-dimensional spaces. We demonstrate here how to apply the toolset of persistent homology to the extraction of novel clustering signatures and the identification of long-range flow correlations in the particle production process of nuclear collisions.