Reconstruction of Short-Lived Particles using Graph-Hypergraph Representation Learning

Callum Birch-Sykes, Brian Le, Yvonne Peters, Ethan Simpson, Zihan Zhang

Research output: Working paperPreprint

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Abstract

In collider experiments, the kinematic reconstruction of heavy, short-lived particles is vital for precision tests of the Standard Model and in searches for physics beyond it. Performing kinematic reconstruction in collider events with many final-state jets, such as the all-hadronic decay of topantitop quark pairs, is challenging. We present HyPER, a graph neural network that uses blended graph-hypergraph representation learning to reconstruct parent particles from sets of final-state objects. HyPER is tested on simulation and shown to perform favorably when compared to existing state-of-the-art reconstruction techniques, while demonstrating superior parameter efficiency. The novel hypergraph approach allows the method to be applied to particle reconstruction in a multitude of different physics processes.
Original languageEnglish
Publication statusPublished - 15 Feb 2024

Keywords

  • hep-ph

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