2 papers
hep-ph2023
Unsupervised learning in the metric space of jets
Tejes Gaertner, Jared Reiten
In the first part of this work, we demonstrate how the metric space structure induced by the energy mover's distance can be leveraged for the unsupervised tagging of jets according…
hep-ph2023
The simplicial substructure of jets
Tejes Gaertner, Jared Reiten
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…