AI 中文总结
该研究将大型归一化流的似然蒸馏为轻量学生估计器，实现LHC硬件触发的实时异常检测，提升了物理性能并降低了延迟。
AI 中文摘要
归一化流是一种原理性的异常检测器，可通过逐事件概率似然选择异常。极端延迟和资源限制阻碍了在大型强子对撞机（LHC）的硬件触发中部署流似然。我们通过将大型归一化流的似然蒸馏为适合在现场可编程门阵列（FPGA）上部署的轻量学生估计器，绕过了这些限制。我们使用条件归一化流，能在存在缺失输入特征时进行精确的似然估计。我们考虑了决策树和神经网络两种学生模型，其中神经网络学生在高级量化技术下进行优化。通过同时提升似然质量并降低推理成本，我们证明与现有基于流的方法相比，该方法兼具最先进的物理性能和延迟表现。
英文摘要
Normalizing flows are principled anomaly detectors, selecting anomalies using a probabilistic per-event likelihood. Extreme latency and resource constraints have prevented the deployment of flow likelihoods within the hardware triggers at the Large Hadron Collider. We bypass these limitations by distilling the likelihood from a large normalizing flow into lightweight student estimators suitable for deployment on a field-programmable gate array. Our use of a conditional normalizing flow enables precise likelihood estimation in the presence of missing input features. Both decision tree and neural network students are considered, the latter optimized under advanced quantization techniques. By simultaneously improving likelihood quality and lowering inference cost, we demonstrate both state-of-the-art physics performance and latency compared with existing flow-based approaches.
