In analyses conducted at Belle II, it is often beneficial to reconstruct the entire decay chain of both B mesons produced in an electron-positron collision event using the information gathered from detectors. The currently used reconstruction algorithm, starting from the final state particles, consists of multiple stages that necessitate manual configurations and suffers from low efficiency and a high number of wrongly reconstructed candidates. Within the Hypertagging project, we are developing software with the goal of automatically reconstructing B decays at Belle II with both high efficiency and accuracy. The trained models should be capable of accommodating rare decays with very small branching ratios, or even those that are unseen during the training phase. To ensure optimal performance, the project is divided into the steps embedding of particles, particle reconstruction, and link prediction. Transformers and hyperbolic embedding are employed as fundamental components, with metric learning serving as the primary training technique. In this paper, we present a proof-of-concept evaluation of the Hypertagging on a toy dataset.
Yu et al. (Tue,) studied this question.