Probabilistic, Structure-Intrinsic Clustering with an Hierarchical Embedding (PSICHE) in Time and Space Applied In Particle Physics Jet Reconstruction

Abstract

Jet reconstruction is an active and open area of particle physics research, with questions related to jet size, multiplicity, substructure, and experimental performance in the presence of pileup and noise. Analogous challenges are ubiquitous throughout other applications of unsupervised learning in the context of clustering and association. This paper introduces a new algorithm for clustering in the context of jet reconstruction: Probabilistic, Structure-Intrinsic Clustering with an Hierarchical Embedding (PSICHE). PSICHE addresses several challenges and shortcomings of existing jet clustering methods with novel features, including but not limited to: (i) dynamically-learned and variable jet (cluster) 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-consistent, probabilistic framework that is computationally tractable. Furthermore, demonstrations of these features are presented for a range of jet clustering examples in LHC scenarios for various physics phenomena, clustering inputs, pileup conditions, and detector performance parameters.