English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Jet clustering is traditionally an unsupervised learning task because thereis no unique way to associate hadronic final states with the quark and gluondegrees of freedom that generated them. However, for uncolored particles like$W$, $Z$, and Higgs bosons, it is possible to approximately (though notexactly) associate final state hadrons to their ancestor. By labeling simulatedfinal state hadrons as descending from an uncolored particle, it is possible totrain a supervised learning method to create boson jets. Such a method muchoperates on individual particles and identifies connections between particlesoriginating from the same uncolored particle. Graph neural networks arewell-suited for this purpose as they can act on unordered sets and naturallycreate strong connections between particles with the same label. These networksare used to train a supervised jet clustering algorithm. The kinematicproperties of these graph jets better match the properties of simulatedLorentz-boosted $W$ bosons. Furthermore, the graph jets contain moreinformation for discriminating $W$ jets from generic quark jets. This workmarks the beginning of a new exploration in jet physics to use machine learningto optimize the construction of jets and not only the observables computed fromjet constituents.

Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons