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.

翻译：漂移室长期以来一直是对撞机径迹探测的核心部件，但未来如希格斯工厂等装置需要更高的探测单元密度和簇计数技术以进行粒子识别，这带来了新的数据处理挑战。在“边缘”端（即探测单元级读出系统）部署机器学习技术，可通过在数据源头执行簇计数，显著降低高密度漂移室的离探测器数据率。本文提出了适用于未来漂移室实时读出的簇计数机器学习算法。这些算法基于可实现的π介子-kaon介子分离能力，性能优于传统的基于导数的方法。当在FPGA硬件资源上实现时，这些算法能够满足未来希格斯工厂场景下实时操作的延迟要求，从而不仅推动了未来对撞机探测器的研发，也促进了高能物理领域中基于硬件的边缘机器学习应用的发展。