Speaker
Description
In high-energy collider physics, the reconstruction of complicated event topologies, such as fully hadronic $t\bar{t}$ decays, is a difficult challenge for many analyses due to the large combinatorial backgrounds. While previous machine learning methods have advanced this task, they struggle to learn the exact joint distribution, and instead learn various independent marginal approximations.
To address this shortcoming, 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 factorized decomposition of decay products. We demonstrate state-of-the-art performance on fully hadronic $t\bar{t}$ decays. Pairton provides a general, flexible paradigm for particle reconstruction that can be readily extended to other topologies, bridging ideas from modern generative modeling and high-energy physics.