A hierarchical optimal transport distance, built by measuring events as distributions of jets whose shapes are measured by optimal transport, improves classification of simulated LHC events.
Classification of Energy Flow Observables in Narrow Jets
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abstract
We present a classification of energy flow variables for highly collimated jets. Observables are constructed by taking moments of the energy flow and forming scalars of a suitable Lorentz subgroup. The jet shapes are naturally arranged in an expansion in both angular and energy resolution, allowing us to derive the natural observables for describing an N-particle jet. We classify the leading variables that characterize jets with up to 4 particles. We rediscover the familiar jet mass, angularities, and planar flow, which dominate the lowest order substructure variables. We also discover several new observables and we briefly discuss their physical interpretation.
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Multi-scale Optimal Transport for Complete Collider Events
A hierarchical optimal transport distance, built by measuring events as distributions of jets whose shapes are measured by optimal transport, improves classification of simulated LHC events.