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This research applies hypergraph representation learning via graph neural networks to improve the kinematic reconstruction of short-lived particles in high-energy collider experiments. The proposed HyPER architecture demonstrates superior parameter efficiency and accuracy compared to existing techniques, particularly for complex processes like top-antitop quark pair decays. By bridging advanced machine learning methods with fundamental particle physics analysis, this work offers a scalable solution for precision tests of the Standard Model and searches for new physics.
Reconstructing short-lived particles using hypergraph representation learning
arXiv:2402.10149v3 Announce Type: cross Abstract: In collider experiments, the kinematic reconstruction of heavy, short-lived particles is vital for precision tests of the Standard Model and in searches for physics beyond it. Performing kinematic reconstruction in collider events with many final-state jets, such as the all-hadronic decay of top-antitop quark pairs, is challenging. We present HyPER: Hypergraph for Particle Event Reconstruction, a novel architecture based on graph neural networks that uses hypergraph representation learning to build more powerful and efficient representations of collider events. HyPER is used to reconstruct parent particles from sets of final-state objects. Trained and tested on simulation, the HyPER model is shown to perform favorably when compared to existing state-of-the-art reconstruction techniques, while demonstrating superior parameter efficiency. The novel hypergraph approach allows the method to be applied to particle reconstruction in a multitude of different physics processes.
Abstract:In collider experiments, the kinematic reconstruction of heavy, short-lived particles is vital for precision tests of the Standard Model and in searches for physics beyond it. Performing kinematic reconstruction in collider events with many final-state jets, such as the all-hadronic decay of top-antitop quark pairs, is challenging. We present HyPER: Hypergraph for Particle Event Reconstruction, a novel architecture based on graph neural networks that uses hypergraph representation learning to build more powerful and efficient representations of collider events. HyPER is used to reconstruct parent particles from sets of final-state objects. Trained and tested on simulation, the HyPER model is shown to perform favorably when compared to existing state-of-the-art reconstruction techniques, while demonstrating superior parameter efficiency. The novel hypergraph approach allows the method to be applied to particle reconstruction in a multitude of different physics processes.
13 pages, 10 figures. Published by Physical Review D (PRD)
High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)
arXiv:2402.10149 [hep-ph]
(or arXiv:2402.10149v3 [hep-ph] for this version)
https://doi.org/10.48550/arXiv.2402.10149 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1103/PhysRevD.111.032004 Focus to learn more DOI(s) linking to related resources
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