Learning transferable event representations for charmed baryon physics at BESIII

Abstract

Deep learning has become an essential tool in high-energy physics, where the ability to learn transferable event representations can significantly improve model generalization across related physics processes. In this work, we present a Particle Transformer-based framework for learning such representations for charmed baryon physics in the BESIII experiment. The framework is implemented through large-scale pre-training on Monte Carlo simulation samples and subsequent fine-tuning for downstream analyses. Using the production and decays of the charmed baryon $\Lambda_c^+$ as a benchmark, we develop pre-trained models for both event classification and momentum-direction regression. The classification model learns discriminative event representations for the dominant physics categories, rejecting 97.0\% of background events at a signal efficiency of 90.0\%. Across 12 benchmark $\Lambda_c^+$ decay channels, fine-tuning from the pre-trained model achieves performance comparable or better than training from scratch, with particularly clear improvements in low-statistics regimes. For the regression task, the pre-trained model improves the momentum-direction prediction across the same benchmark channels. Further improvement is obtained after fine-tuning in the representative semileptonic decay $\Lambda_c^+ \to p K^- e^+ \nu_e$. This strategy provides a scalable solution for a wide range of physics cases at BESIII and can be extended to other high energy experiments.