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Learning Feynman Diagrams using Graph Neural Networks
Alexander Norcliffe · Harrison Mitchell · Pietro Lió

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in Machine Learning and the Physical Sciences »

In the wake of the growing popularity of machine learning in particle physics, this work finds a new application of geometric deep learning on Feynman diagrams to make accurate and fast matrix element predictions with the potential to be used in analysis of quantum field theory. This research uses the graph attention layer which makes matrix element predictions to 1 significant figure accuracy above 90% of the time. Peak performance was achieved in making predictions to 3 significant figure accuracy over 10% of the time with less than 200 epochs of training, serving as a proof of concept on which future works can build upon for better performance. Finally, a procedure is suggested, to use the network to make advancements in quantum field theory by constructing Feynman diagrams with effective particles that represent non-perturbative calculations.

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Author Information

Alexander Norcliffe (University of Cambridge)

I'm a PhD student in Machine Learning for Medicine. I am co-supervised by Mihaela Van der Schaar and Pietro Lio in the Cambridge Centre for AI in Medicine.

Harrison Mitchell (University of Cambridge)
Pietro Lió (University of Cambridge)

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