AI 中文总结
针对 Belle II 中央漂移室前端电子学串扰噪声问题，开发基于机器学习的波形鉴别方法，在赛灵思 Virtex-5 FPGA 中实现紧凑快速的增强决策树模型，有效降低噪声，验证了该方法在探测器前端电子学中应用的可行性和潜力。
AI 中文摘要
机器学习推理在对撞机实验实时触发中广泛用于探测器特征识别。相比之下，机器学习在前端电子学中的应用未充分探索，主要受限于现场可编程门阵列资源、功耗和局部探测器覆盖范围等。本文为 Belle II 实验的中央漂移室开发基于机器学习的波形鉴别方法以抑制前端串扰噪声。Belle II 中央漂移室是离线和实时硬件触发的关键带电粒子跟踪探测器。运行中，在中央漂移室前端电子学中观察到背景线击中，硬件跟踪触发采用基于多线层击中组合形成的轨道段的霍夫变换。由于信息减少，跟踪触发对串扰噪声敏感，未来高亮度下假触发率会增加。我们在中央漂移室前端电子学的赛灵思 Virtex-5 现场可编程门阵列中实现紧凑快速的增强决策树模型，以全流水线方式独立处理每个线通道的波形。离线研究表明，串扰噪声可降低约两倍，同时信号效率保持在 98%以上。在 Belle II 专用校准运行期间的固件验证表明，轨道段和触发率最多可降低 50%，同时保持对含轨道事件 10%以内的触发接受度。这项工作证明了在探测器前端电子学中紧凑低延迟机器学习推理的技术可行性，并突出了其在未来高能物理实验智能探测器读出系统中的潜力。
英文摘要
Machine learning (ML) inference on FPGAs has been widely adopted in real-time triggering of collider experiments for detector signature identification. In contrast, the ML application in Front-End Electronics (FEE) has not yet been fully explored, primarily due to constraints such as limited FPGA resources, power consumption, and localized detector coverage. In this work, we develop an ML-based waveform discrimination method for the Central Drift Chamber (CDC) of the Belle II experiment to suppress cross-talk noise at the front-end level. The Belle II CDC is a key charged-particle tracking detector for both offline and the real-time hardware trigger. During Belle II operation, background wire hits have been observed in the CDC FEE, where multiple hits occur in neighboring anode wires by large energy deposit. The hardware track trigger employs a Hough transformation based on track segments formed by combining hits from multiple wire layers. Due to the reduced information, the track trigger is sensitive to cross-talk noise, hence resulting in an increased fake trigger rate with higher luminosity in the future. We employ compact and fast Boosted Decision Tree models implemented in a Xilinx Virtex-5 FPGA of the CDC FEE, where waveform is processed independently for each wire channel in a fully pipelined manner. Offline studies show that the cross-talk noise can be reduced by approximately a factor of two while maintaining a signal efficiency above 98%. The firmware validation during dedicated Belle II calibration runs demonstrated reductions of up to 50% in track segment and trigger rates while preserving the trigger acceptance for events containing tracks within 10%. This work demonstrates the technical feasibility of compact and low-latency ML inference in detector FEE and highlights its potential for future intelligent detector readout systems in high-energy physics experiments.
Comments8 pages, 9 figures, submitted to IEEE Transaction on Nuclear Science
