Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm

Understand

Hadronic signals of new-physics origin at the Large Hadron Collider can remain hidden within the copiously produced hadronic jets.

• Unveiling such signatures require highly performant deep-learning algorithms.
• We construct a class of Graph Neural Networks (GNN) in the message-passing formalism that makes the network output infra-red and collinear (IRC) safe, an important criterion satisfied within perturbative QCD calculations.
• Including IRC safety of the network output as a requirement in the construction of the GNN improves its explainability and robustness against theoretical uncertainties in the data.