用图神经网络和视觉变换器加速μ子追踪，提升实时处理效率。
Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer
- 将图神经网络嵌入重建流程，提升背景干扰剔除效率。
- 利用视觉变换器实现2.3毫秒内完成98%精度的μ子追踪。
- 适合高亮度对撞机时代需要高速数据处理的研究者。
在大型强子对撞机的ATLAS实验中，识别与重建带电粒子（如μ子）是核心挑战。随着2030年后高亮度对撞机时代的到来，每束团碰撞次数将从60增至最多200次，导致ATLASμ子谱仪中的事件密度上升，对触发系统中的事件过滤器提出更高要求。为此，本文提出两种基于机器学习的方法：首先，将图神经网络集成到非机器学习基准重建链中，实现背景击中剔除，重建速度从255毫秒提升至217毫秒，提速15%；其次，首次展示基于前沿视觉变换器架构的端到端μ子追踪方法，在消费级GPU上仅需2.3毫秒即可完成近似重建，效率达98%。该工作为未来高密度环境下的实时数据处理提供了新路径。
原文摘要 · Abstract (English)
The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This task will become increasingly difficult with the start of the High-Luminosity LHC era after 2030, when the number of proton-proton collisions per bunch crossing will increase from 60 to up to 200. This elevated interaction density will also increase the occupancy within the ATLAS Muon Spectrometer, requiring more efficient and robust real-time data processing strategies within the experiment's trigger system, particularly the Event Filter. To address these algorithmic challenges, we present two machine-learning-based approaches. First, we target the problem of background-hit rejection in the Muon Spectrometer using Graph Neural Networks integrated into the non-ML baseline reconstruction chain, demonstrating a 15 % improvement in reconstruction speed (from 255 ms to 217 ms). Second, we present a proof-of-concept for end-to-end muon tracking using state-of-the-art Vision Transformer architectures, achieving ultra-fast approximate muon reconstruction in 2.3 ms on consumer-grade GPUs at 98 % tracking efficiency.
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