{"ID":22952772,"CreatedAt":"2026-09-17T02:12:05.498442134Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.18928","arxiv_id":"2609.18928","title":"Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion","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.","short_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 particl...","url_abs":"https://arxiv.org/abs/2609.18928","url_pdf":"https://arxiv.org/pdf/2609.18928v1","authors":"[\"Lining Mao\",\"Yvonne Peters\",\"Ethan Simpson\",\"Zihan Zhang\"]","published":"2026-09-16T17:03:37Z","proceeding":"hep-ph","tasks":"[\"hep-ph\",\"cs.LG\",\"hep-ex\"]","methods":"[\"Diffusion Model\"]","has_code":false}