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Do graph neural networks learn jet substructure?
Farouk Mokhtar · Raghav Kansal · Javier Duarte

At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural networks have been used to address this task by treating jets as point clouds with underlying, learnable, edge connections between the particles inside. We explore the decision-making process for one such state-of-the-art network, ParticleNet, by looking for relevant edge connections identified using the layerwise-relevance propagation technique. As the model is trained, we observe changes in the distribution of relevant edges connecting different intermediate clusters of particles, known as subjets. The resulting distribution of subjet connections is different for signal jets originating from top quarks, whose subjets typically correspond to its three decay products, and background jets originating from lighter quarks and gluons. This behavior indicates that the model is using traditional jet substructure observables, such as the number of prongs—energetic particle clusters—within a jet, when identifying jets.

Author Information

Farouk Mokhtar (UC San Diego)
Raghav Kansal (UC San Diego)
Javier Duarte (UC San Diego)

I am an Assistant Professor in experimental high energy physics at UC San Diego and a member of the CMS collaboration at CERN. My research interests include measuring the properties and couplings of the Higgs boson and searching for beyond-the-standard-model particles in LHC data. I am interested in developing machine learning algorithms, real-time trigger systems (with applications to embedded devices), and heterogenous computing architectures for the next generation of high energy physics experiments.

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