Recognition: unknown
Better Jet Clustering Algorithms
read the original abstract
We investigate modifications to the $k_\perp$-clustering jet algorithm which preserve the advantages of the original Durham algorithm while reducing non-perturbative corrections and providing better resolution of jet substructure. We find that a simple change in the sequence of clustering (combining smaller-angle pairs first), together with the `freezing' of soft resolved jets, has beneficial effects.
This paper has not been read by Pith yet.
Forward citations
Cited by 5 Pith papers
-
The anti-k_t jet clustering algorithm
The anti-k_t algorithm yields conical jets with equal active and passive areas, zero area anomalous dimensions, rigid-boundary non-global logarithms, and a universal Milan factor, serving as an IRC-safe substitute for...
-
Logarithmically-accurate showers with massive quarks
PanScales final-state showers now include quark masses at NLL accuracy while keeping original accuracy for massless observables.
-
Physics inspired quantum algorithm for QCD splitting functions
A modular two-qubit quantum circuit is constructed to encode the concurrence of helicity entanglement in pure-gluon splitting, with parameters calibrated to LHC jet data so that composed circuits reproduce experimenta...
-
Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across...
- Looking inside jets: an introduction to jet substructure and boosted-object phenomenology
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.