Speaker
Description
Jet reconstruction is an active and open area of particle physics research, with challenges and questions related to jet size, multiplicity, substructure, and experimental performance in the presence of pileup and noise. A new algorithm, Probabilistic, Structure-Intrinsic Clustering with an Hierarchical Embedding (PSICHE, arxiv:2608.xxxx), introduces a variety novel features to the domains of jet clustering and jet substructure, addressing several challenges and shortcomings of existing methods. These features include, but are not limited to: (i) dynamically-learned and variable jet sizes, (ii) unsupervised, multi-scale learning of emergent features, like jet substructure and multiplicity, (iii) clustering in space and time, and (iv) incorporation of domain-specific experimental uncertainties. All of these properties are achieved in a self-contained, probabilistic framework that is computationally tractable.
This contribution will introduce and describe the PSICHE algorithm, with a range of examples at LHC energies for various physics phenomena (QCD jets, resolved/boosted top quarks, boosted Ws), clustering inputs (calorimeter cells, particle candidates), pileup scenarios (current and HL-LHC projected), and detector performance parameters.