在粒子探测器边缘实时计算簇数，提升高能物理数据处理效率
Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers
- 在读出单元直接部署机器学习算法，实现簇数实时计数
- 相比传统方法，π/ K 分离性能显著提升，满足未来对撞机需求
- 可适配FPGA硬件，延迟达标，适合高能物理边缘计算场景
漂移室长期是对撞机追踪的核心组件，但未来如希格斯工厂等装置要求更高分辨率和簇数计数以实现粒子识别，带来新的数据处理挑战。在‘边缘’（即单元级读出）部署机器学习（ML），可通过在源头完成簇数计数，大幅降低离探测器的数据传输速率。本文提出面向未来漂移室实时读出的簇数计数机器学习算法。这些算法在可实现的π/ K 分离性能上优于传统基于导数的方法。在合成至FPGA资源后，其延迟符合未来希格斯工厂场景下的实时运行要求，推动了下一代对撞机探测器研发以及高能物理中基于硬件的边缘机器学习应用。
原文摘要 · Abstract (English)
Drift chambers have long been central to collider tracking, but future machines like a Higgs factory motivate higher granularity and cluster counting for particle ID, posing new data processing challenges. Machine learning (ML) at the "edge", or in cell-level readout, can dramatically reduce the off-detector data rate for high-granularity drift chambers by performing cluster counting at-source. We present machine learning algorithms for cluster counting in real-time readout of future drift chambers. These algorithms outperform traditional derivative-based techniques based on achievable pion-kaon separation. When synthesized to FPGA resources, they can achieve latencies consistent with real-time operation in a future Higgs factory scenario, thus advancing both R&D for future collider detectors as well as hardware-based ML for edge applications in high energy physics.
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