Supervised jet clustering with graph neural networks for Lorentz boosted bosons

Xiangyang Ju,Benjamin Nachman

doi:10.1103/physrevd.102.075014

Xiangyang Ju, Benjamin Nachman

Open Access

https://doi.org/10.1103/physrevd.102.075014

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Abstract

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 approximately (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 create boson jets. Such a method much operates 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.

Highlights

Lorentz-boosted massive bosons are a common feature of theories that extend the Standard Model (SM) of particle physics
This work marks the beginning of a new exploration in jet physics to use machine learning to optimize the construction of jets and the observables computed from jet constituents
Traditional jet clustering based on unsupervised learning has proven to be an effective tool for studying hadronic final states at the LHC

Summary

INTRODUCTION

Lorentz-boosted massive bosons are a common feature of theories that extend the Standard Model (SM) of particle physics. A machine could be trained to label individual particles as originating from a color singlet or not based on the particle kinematic properties as well as the relationship with other particles in the event While such an approach may give up the calculability afforded by algorithms like anti-kt, it may provide an optimal approach to constructing jets for searches where calculability is not necessarily required. To construct a supervised jet clustering algorithm, a machine learning architecture is needed that can process variable length sets as input Multiple such point cloud methods have been studied for jet substructure [70,72,73,78,79,104], but the structure chosen here is the graph neural network (GNN)

SIMULATION

GRAPH NEURAL NETWORK METHODS

RESULTS

CONCLUSIONS