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Pairton applies advanced machine learning techniques, specifically graph-based generative modeling and pairformer architectures, to solve the complex problem of reconstructing short-lived particles in high-energy physics collisions. This work represents a significant intersection where modern AI methods are directly enhancing experimental capabilities within quantum-scale particle physics research. By treating decay relationships as iterative edge predictions on graphs, it establishes a new paradigm for analyzing fully hadronic $t\bar{t}$ decays with state-of-the-art performance.
Pairton: Iterative Reconstruction of Short-Lived Particles
arXiv:2608.14278v1 Announce Type: cross Abstract: We present Pairton, an iterative framework for reconstructing short-lived particles in high-energy collision events. By formulating particle reconstruction as a masked prediction process over graph structures, Pairton learns conditional distributions consistent with a factorised decomposition of decay products and iteratively predicts edges in the adjacency matrix representing particle decay relationships. Leveraging a pairformer-based architecture with dynamically updated pairwise representations, our method incorporates global event consistency. We demonstrate state-of-the-art performance on fully hadronic $t\bar{t}$ decays. Pairton provides a general, flexible paradigm for particle reconstruction and can be readily extended to other topologies, bridging ideas from modern generative modelling and high-energy physics.
Abstract:We present Pairton, an iterative framework for reconstructing short-lived particles in high-energy collision events. By formulating particle reconstruction as a masked prediction process over graph structures, Pairton learns conditional distributions consistent with a factorised decomposition of decay products and iteratively predicts edges in the adjacency matrix representing particle decay relationships. Leveraging a pairformer-based architecture with dynamically updated pairwise representations, our method incorporates global event consistency. We demonstrate state-of-the-art performance on fully hadronic $t\bar{t}$ decays. Pairton provides a general, flexible paradigm for particle reconstruction and can be readily extended to other topologies, bridging ideas from modern generative modelling and high-energy physics.
11 pages, 5 figures, to be submitted to Phys. Rev. D
High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)
arXiv:2608.14278 [hep-ph]
(or arXiv:2608.14278v1 [hep-ph] for this version)
https://doi.org/10.48550/arXiv.2608.14278 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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