Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion
In particle collider experiments, event reconstruction is the task of inferring the kinematics of short-lived particles produced in the hard scatter from the stable final states recorded by detectors. We decompose event reconstruction into two primary tasks: assigning measured jets and charged leptons to parent particles, and predicting unmeasured neutrino kinematics. We present VyPER, a novel geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology. VyPER combines the supervised classification of hyperedges for particle assignment with a diffusion model for predicting neutrino kinematics, leveraging a joint loss function to optimize bot...
| Authors | Lining Mao, Yvonne Peters, Ethan Simpson et al. |
|---|---|
| Primary category | hep-ph |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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