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Learning Feynman Diagrams using Graph Neural Networks

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In the wake of machine learning growing in popularity in the field of 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 above 90% of the time. Peak performance was achieved in making predictions to 3 significant figures over 10% of the time with less than 200 epochs of training. Finally, a procedure is suggested, to use the network to make advancement in quantum field theory by constructing Feynman diagrams with effective particles that represent non-perturbative calculations.


This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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