Which Metric on the Space of Collider Events?
arXiv:2111.03670 · doi:10.1103/PhysRevD.105.076003
Abstract
Which is the best metric for the space of collider events? Motivated by the success of the Energy Mover's Distance in characterizing collider events, we explore the larger space of unbalanced optimal transport distances, of which the Energy Mover's Distance is a particular case. Geometric and computational considerations favor an unbalanced optimal transport distance known as the Hellinger-Kantorovich distance, which possesses a Riemannian structure that lends itself to efficient linearization. We develop the particle linearized unbalanced Optimal Transport (pluOT) framework for collider events based on the linearized Hellinger-Kantorovich distance and demonstrate its efficacy in boosted jet tagging. This provides a flexible and computationally efficient optimal transport framework ideally suited for collider physics applications.
arXiv comment17 pages, 5 figures, 3 tables
References in corpus (31)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- An Introduction to PYTHIA 8.2
- FastJet user manual
- Identifying Boosted Objects with N-subjettiness
- Performance of pile-up mitigation techniques for jets in collisions at TeV using the ATLAS detector
- Towards Jetography
- Maximizing Boosted Top Identification by Minimizing N-subjettiness
- Pileup Per Particle Identification
- Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches
- Techniques for improved heavy particle searches with jet substructure
- Energy Flow Networks: Deep Sets for Particle Jets
- Generalized Wasserstein distance and its application to transport equations with source
- Optimal Entropy-Transport problems and a new Hellinger-Kantorovich distance between positive measures
- Energy flow polynomials: A complete linear basis for jet substructure
- The Metric Space of Collider Events
- Pileup Mitigation with Machine Learning (PUMML)
- SoftKiller, a particle-level pileup removal method
- Particle-level pileup subtraction for jets and jet shapes
- Optimal transport in competition with reaction: the Hellinger-Kantorovich distance and geodesic curves
- Exploring the Space of Jets with CMS Open Data
- Jet Cleansing: Pileup Removal at High Luminosity
- Unnormalized Optimal Transport
- A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
- The Hidden Geometry of Particle Collisions
- The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
- Linearized Optimal Transport for Collider Events
- Pileup mitigation at the LHC: a theorist's view
- The Efficacy of Event Isotropy as an Event Shape Observable
- Cutting Multiparticle Correlators Down to Size
- An Exploration of Learnt Representations of W Jets
- The Linearized Hellinger--Kantorovich Distance
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- Disentangling Quarks and Gluons with CMS Open Data
- SHAPER: Can You Hear the Shape of a Jet?
- Neural Embedding: Learning the Embedding of the Manifold of Physics Data
- Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations
- Earth mover's distance as a measure of CP violation
- Pileup and Infrared Radiation Annihilation (PIRANHA): A Paradigm for Continuous Jet Grooming
- A Field Guide to Event-Shape Observables Using Optimal Transport