DecayFM: A Physics-Structured Foundation Model for Data-Efficient Rare Particle Decay Identification

Identifying particle decay modes from reconstructed detector measurements is essential to high-energy physics, but building separate classifiers for many channels is costly, particularly when downstream training samples are limited. This setting presents two challenges for transferable representation learning: heterogeneous physical measurements contribute differently across decay modes, and highly imbalanced training frequencies can bias learning toward common channels. We introduce DecayFM, a physics-structured foundation model for data-efficient particle decay identification. DecayFM separately encodes kinematic, energy-deposition, and particle-identification features, integrates them through a particle-set Transformer, and applies paired low-rank adapters whose contributions are weighted by a learned head–tail router. A two-stage training procedure separates shared representation learning from frequency-aware adaptation. We construct the ParticleDecayMode (PDM) dataset series using BESIII simulation, comprising approximately 8.3 million $$\begin{gathered} J \\ !/ \\ ! \\ psi \end{gathered}$$ decay events for large-scale pretraining and downstream evaluation, including a benchmark of 200 rare decay modes. On rare-mode classification tasks, pretrained DecayFM improves accuracy over the reported from-scratch baseline by up to 11.61 percentage points. In the evaluated few-shot settings, parameter-efficient adaptation with 10-100 training events per class exceeds the accuracy of the corresponding baseline trained with 1,000 events per class. These results support physics-structured pretraining as a promising approach to reducing downstream training-data requirements for rare decay identification.