Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion
VyPER framework reconstructs collider events using hypergraph representation learning and graph-conditioned diffusion, outperforming existing reconstruction techniques across Standard Model processes.
Researchers present VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology for particle event reconstruction. It combines supervised hyperedge classification for assigning measured jets and charged leptons to parent particles with a graph-conditioned diffusion model predicting unmeasured neutrino kinematics, optimized with a joint loss. Evaluated across several proton-proton collision processes, it demonstrates accurate reconstruction across Higgs, electroweak, and top-quark sectors.
- Represents collider events as hypergraphs with physics-inspired topology
- Hyperedge classification assigns jets and leptons to parent particles
- Graph-conditioned diffusion model predicts unmeasured neutrino kinematics under a joint loss
- Products
- VyPER
From the original article
1 min read at arxiv.orgIn 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.
By Lining Mao · arxiv.org
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