用Transformer模型直接从轨迹和簇重建粒子，提升CMS事件重构精度。
Machine-learning based particle-flow algorithm in CMS
- 用Transformer端到端建模，一次推理完成粒子重建
- 在CMS数据上实现更高精度的粒子动量与能量重建
- 适合高能物理实验中追求高效精准重构的团队
粒子流（PF）算法通过重建末态粒子提供全局事件描述，是CMS实验事件重建的核心。近年来，端到端机器学习方法被提出，以直接优化关注的物理量并利用异构计算架构。其中，机器学习粒子流（MLPF）采用Transformer模型，仅需单次前向传播即可从轨迹和簇中直接推断粒子。本文介绍CMS在MLPF方面的最新进展，包括训练数据集、模型架构、重建指标以及与离线重建软件的集成。
原文摘要 · Abstract (English)
The particle-flow (PF) algorithm provides a global event description by reconstructing final-state particles and is central to event reconstruction in CMS. Recently, end-to-end machine learning (ML) approaches have been proposed to directly optimize physical quantities of interest and to leverage heterogeneous computing architectures. One such approach, machine-learned particle flow (MLPF), uses a transformer model to infer particles directly from tracks and clusters in a single pass. We present recent CMS developments in MLPF, including training datasets, model architecture, reconstruction metrics, and integration with offline reconstruction software.
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