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The simplicial substructure of jets

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abstract

In this work, we construct a new data type for hadronic jets in which the traditional point-cloud representation is transformed into a simplicial complex consisting of vertices, or 0-simplexes. An angular resolution scale, $r$, is then drawn about each vertex, forming balls about hadrons. As $r$ grows, the overlap of balls form 2- and 3-point connections, thereby appending 1- and 2-simplexes to the complex. We thus associate a jet with an angular-resolution-dependent characterization of its substructure -- we dub this data type the simplicial substructure complex $K_{\rm sub}(r)$. This data type gives rise to two interesting representations. First, the subset of 0-and 1-simplexes lends itself naturally to a graph representation of a given jet's substructure and we provide examples of valuable graph-theoretic calculations such a representation affords. Second, the subset of 1- and 2-simplexes gives rise to what is known as a Face-Counting-Vector, in topological combinatorics parlance. We explore information-theoretic aspects of the components of this vector, various metric properties which follow, as well as how this vector can be used to define new jet-shape observables. The utility of these representations is demonstrated in the context of the discrimination of jets initiated by light quarks and gluons from those initiated by tops.

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2025 1

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representative citing papers

Towards a topological data analysis for heavy-ion collisions

nucl-th · 2025-09-02 · 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 observables.

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  • Towards a topological data analysis for heavy-ion collisions nucl-th · 2025-09-02 · conditional · none · ref 27 · internal anchor

    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 observables.