Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion

arXiv:2609.18928 · hep-ph, cs.LG, hep-ex · Submitted 2026-09-16 · Read on arXiv

hep-ph, cs.LG, hep-ex

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: 23 pages, 9 figures, to be submitted to PRX Intelligence

Code: https://github.com/els285/VyPERpaper

License: http://creativecommons.org/licenses/by/4.0/

The gist: 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.

Terminology

Abstract

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.

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