Pairton: Iterative Reconstruction of Short-Lived Particles
For high-energy physics researchers, Pairton offers a new general paradigm for particle reconstruction, though its demonstrated gains are specific to one decay topology.
Pairton introduces an iterative graph-based framework for reconstructing short-lived particles in high-energy collisions, achieving state-of-the-art performance on fully hadronic t-tbar decays.
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.