在CMS实验的实时触发系统中实现毫微秒级异常事件检测。
Real-time Anomaly Detection at the L1 Trigger of CMS Experiment
- 用自编码器在50纳秒内完成每事件的异常检测。
- 在40 MHz碰撞率下成功部署，识别潜在新物理信号。
- 基于测试机架验证算法，不干扰真实数据采集。
本文介绍在大型强子对撞机（LHC）Run 3期间，为CMS实验全局触发（GT）测试机箱中的FPGA部署并测试一个自编码器模型，用于无偏见地探测新物理信号。全局触发系统每50纳秒需在40兆赫兹的碰撞速率下做出读出或丢弃数据的最终决策。神经网络在此约束条件下对每个事件进行预测，可用于筛选异常事件进行后续分析。测试机箱是主触发系统的副本，接收相同输入数据，但其输出不用于实际触发，从而可在不影响正常采样情况下对新触发算法进行真实数据上的全面测试。我们描述了实现超低延迟异常检测的方法，展示了深度神经网络在测试机箱中的集成，并报告了在质子碰撞期间的监控、测试与验证结果。
原文摘要 · Abstract (English)
We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rate of 40 MHz, within a 50 ns latency. The Neural Network makes a prediction for each event within these constraints, which can be used to select anomalous events for further analysis. The GT test crate is a copy of the main GT system, receiving the same input data, but whose output is not used to trigger the readout of CMS, providing a platform for thorough testing of new trigger algorithms on live data, but without interrupting data taking. We describe the methodology to achieve ultra low latency anomaly detection, and present the integration of the DNN into the GT test crate, as well as the monitoring, testing, and validation of the algorithm during proton collisions.
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