Fuzzy Jets
arXiv:1509.02216 · doi:10.1007/JHEP06(2016)010
Abstract
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.
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Cited by in corpus (12)
- Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
- JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
- Quantum Algorithms for Jet Clustering
- Deep-Learning Jets with Uncertainties and More
- Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
- SHAPER: Can You Hear the Shape of a Jet?
- Dynamic Radius Jet Clustering Algorithm
- Unsupervised and lightly supervised learning in particle physics
- Automatic detection of boosted Higgs boson and top quark jets in an event image
- Telescoping jet substructure
- Jet Substructure Probe on Scalar Leptoquark Models via Top Polarization
- Improving sensitivity of vectorlike top partner searches with jet substructure