AI 中文总结
本研究提出基于Particle Transformer的框架，经大规模预训练和微调，在BESIII实验粲重子物理的分类与回归任务中提升性能，低统计量区间优势明显，可扩展至其他高能实验。
AI 中文摘要
深度学习已成为高能物理领域的重要工具，学习可迁移事例表示的能力可显著提升模型在相关物理过程间的泛化性。本研究提出一种基于Particle Transformer的框架，用于学习BESIII实验粲重子物理的可迁移事例表示。该框架通过在蒙特卡罗模拟样本上进行大规模预训练，再针对下游分析进行微调实现。以粲重子Λ_c^+的产生与衰变作为基准，开发了用于事例分类和动量-方向回归的预训练模型。分类模型学习主导物理类别的判别性事例表示，在信号效率为90.0%时可排除97.0%的本底事例。在12个基准Λ_c^+衰变道上，从预训练模型微调的性能与从头训练相当或更优，在低统计量区间的提升尤为明显。对于回归任务，预训练模型在相同基准道上改善了动量-方向预测，在代表性半轻衰变Λ_c^+→p K^- e^+ ν_e中微调后进一步提升。该策略为BESIII的广泛物理案例提供了可扩展的解决方案，也可扩展至其他高能实验。
英文摘要
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 $Λ_c^+$ 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 $Λ_c^+$ 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 $Λ_c^+ \to p K^- e^+ ν_e$. This strategy provides a scalable solution for a wide range of physics cases at BESIII and can be extended to other high energy experiments.
Comments18 pages, 7 figures, 2 tables
