用改进的Transformer提升高能物理粒子轨迹重建精度与效率
TrackFormers Part 2: Enhanced Transformer-Based Models for High-Energy Physics Track Reconstruction
- 设计新注意力机制与几何投影+轻量聚类结合架构
- 联合模型通过回归预测指导分类，提升轨迹匹配准确率
- 适配多种物理过程的新数据集，支持逐击点训练
高能物理实验产生的数据量正急剧增长，这一趋势将在未来高亮度LHC升级后进一步加剧。数据处理流程亟需优化，粒子轨迹重建是关键改进环节。此前我们提出TrackFormers，一种基于Transformer的一次性编码器模型，能有效关联探测器击点与预期轨迹。本文在此基础上，深入研究更定制化的Transformer注意力机制，提出结合几何投影与轻量聚类的新架构，并引入联合模型，利用回归器预测结果对分类进行条件控制。此外，我们还构建了支持多物理过程、可在击点级别训练的新数据集。这些改进共同提升了模型的准确性和潜在效率，为下一代高能物理实验提供稳健解决方案。
原文摘要 · Abstract (English)
High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing pipeline, with particle track reconstruction being a prime candidate for improvement. In our previous work, we introduced "TrackFormers", a collection of Transformer-based one-shot encoder-only models that effectively associate hits with expected tracks. In this study, we extend our earlier efforts by conducting detailed investigations into more custom Transformer attention mechanisms, a new design combining geometric projection and lightweight clustering, and a joint model conditioning classification on a regressor's predictions. Furthermore, we discuss new datasets that allow the training on hit level for a range of physics processes. These developments collectively aim to boost both the accuracy and potentially the efficiency of our tracking models, offering a robust solution to meet the demands of next-generation high-energy physics experiments.
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