Speaker
Description
In particle collider experiments, event reconstruction is the task of inferring the kinematics of the short-lived, 'interesting' particles of the hard scatter from the stable final states recorded by detectors. We decompose event reconstruction into two tasks: assigning measured jets and leptons to parent particles, and regressing unmeasured neutrino kinematics. We present a novel geometric learning framework that represents collider events as hypergraphs, achieving state-of-the-art performance in the assignment task. The hypergraph model is interfaced with a generative diffusion model to infer inherently multimodal neutrino kinematics distributions. We showcase the approach in several proton-proton collision processes, demonstrating new avenues for analysis in Higgs, electroweak boson and top-quark physics.