用AI实现实时数据处理，提升重味粒子探测效率
Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors
- 用图神经网络与FPGA加速，在3MHz碰撞中实时识别低动量重味事件
- 突破15kHz触发率限制，实现sPHENIX追踪系统高效数据流处理
- 方案可迁移至EIC等未来实验，适合高能物理实时处理研究者
本研究由美国能源部核物理人工智能计划于2022年启动，旨在应对高能核物理实验（如RHIC、LHC及未来EIC）中的数据处理挑战。重点开发sPHENIX实验追踪探测器的实时数据处理原型系统。通过在追踪系统中引入流式技术，可克服量能器15 kHz最大触发率的限制。该方法利用图神经网络（GNN）与面向机器学习的高层次综合（hls4ml），在每秒300万次质子-质子碰撞（3 MHz）的高事件率下，高效识别低动量稀有重味事件。sPHENIX上的成功将显著减少资源消耗并加快重味测量。该方法具备跨实验可移植性。针对EIC，我们构建了基于人工智能-机器学习（AI-ML）算法的DIS电子判别器，实现原位实时识别，展示了AI与FPGA技术在高能核物理与粒子实验实时数据处理流水线中的变革潜力。
原文摘要 · Abstract (English)
This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.
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