Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
Xiangyang Ju, Benjamin Nachman
Jet clustering is traditionally an unsupervised learning task because there is no unique way to associate hadronic final states with the quark and gluon degrees of freedom that generated them. However, for uncolored particles like W, Z, and Higgs bosons, it is possible to precisely (though not exactly) associate final state hadrons to their ancestor. By labeling simulated final state hadrons as descending from an uncolored particle, it is possible to train a supervised learning method to learn to create boson jets. Such a method much operate on individual particles and identifies connections between particles originating from the same uncolored particle. Graph neural networks are well-suited for this purpose as they can act on unordered sets and naturally create strong connections between particles with the same label. These networks are used to train a supervised jet clustering algorithm. The kinematic properties of these graph jets better match the properties of simulated Lorentz-boosted W bosons. Furthermore, the graph jets contain more information for discriminating W jets from generic quark jets. This work marks the beginning of a new exploration in jet physics to use machine learning to optimize the construction of jets and not only the observables computed from jet constituents.