超级KEKB对撞机上 Belle II 实验中图形神经网络电磁量能器触发器的调试与低延迟运行
Commissioning and Low Latency Operation of the Graph Neural Network Electromagnetic Calorimeter Trigger at the Belle II Experiment
AI总结：
介绍 Belle II 实验中 GNN-ETM 的调试与运行，该模块以量能器触发单元为图节点处理数据，经硬件-算法协同设计优化架构，实现低延迟，具备在线控制和监测功能，满足 Belle II 一级触发系统延迟要求。
AI中文摘要：
我们展示了超级KEKB对撞机上 Belle II 实验的图形神经网络电磁量能器触发模块（GNN-ETM）的调试与运行。GNN-ETM 将量能器触发单元作为图形节点进行处理，以执行聚类和特征提取。我们将该系统与一级触发的后续阶段完全集成，开发慢控驱动程序，并添加在线监测功能。通过硬件-算法协同设计优化了现有的基于FPGA的架构，实现了1.053微秒的整体系统延迟。我们的硬件实现通过寄存器传输级仿真得到验证，与离线参考模型实现了位精确一致。在线监测能够测量瞬时触发率，为触发级性能研究提供了定量依据。总之，我们报告了GNN-ETM作为一个具有在线控制和监测功能的完全运行的低延迟触发模块，符合 Belle II 一级触发系统的延迟要求。
英文摘要：
We present the commissioning and operation of the Graph Neural Network Electromagnetic Calorimeter Trigger Module (GNN-ETM) of the Belle II experiment at the SuperKEKB collider. The GNN-ETM processes calorimeter trigger cells as graph nodes to perform clustering and feature extraction. We fully integrate the system with the successive stages of the first-level trigger, develop slow-control drivers, and add online monitoring capabilities. We optimise the existing FPGA-based architecture through hardware-algorithm co-design, achieving an overall system latency of 1.053 us. Our hardware implementation is validated through register-transfer-level simulations, achieving bit-accurate agreement with the offline reference model. Online monitoring enables the measurement of instantaneous trigger rates, providing a quantitative basis for trigger-level performance studies. In summary, we report on the GNN-ETM as a fully operational, low-latency trigger module with online control and monitoring capabilities, compatible with the latency requirements of the Belle II first-level trigger system.
