Graph neural networks in particle physics
Jonathan Shlomi, Peter Battaglia, Jean-Roch Vlimant
2020 · Machine Learning Science and Technology · 270 citations
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs—sets of elements and their pairwise relations—and are a central method within the broader field of geometric deep learning. They are very expressive and have demonstrated superior performance to other classical deep learning approaches in a variety of domains. The data in particle physics are often represented by sets and graphs and as such, graph neural networks offer key advantages. Here we review various applications…
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