Hypergraph model improves collider event reconstruction
Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion
This research presents a novel approach to a fundamental problem in high-energy physics, demonstrating a new method for event reconstruction that could improve precision measurements in particle physics.
A new model called VyPER uses hypergraph representation learning to reconstruct particle collider events.
It combines supervised classification for particle assignment with a diffusion model for predicting neutrino kinematics.
The model was tested on proton-proton collision processes and showed improved performance compared to existing techniques.
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 both reconstruction tasks within a unified framework. We showcase VyPER across several proton-proton collision processes, comparing its performance to existing analytical and machine-learning-based reconstruction techniques. In doing so, we demonstrate that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes, opening new avenues for precision measurements in the Higgs boson, electroweak, and top-quark sectors.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include hypergraph, particle physics, event reconstruction, diffusion model.
Source attribution: curated from arXiv cs.LG (@Lining Mao, Yvonne Peters, Ethan Simpson, Zihan Zhang).