标题： 基于脉冲神经网络的LHC物理异常检测 显示英文标题

标题： Anomaly detection with spiking neural networks for LHC physics

摘要： 异常检测为在大型强子对撞机（LHC）上发现新物理提供了一种有前景的策略。 本文研究了使用神经形态脉冲神经网络（SNNs）构建的自动编码器，以实现这一目的。 一个关键应用是在触发级别，其中异常检测工具可以捕捉到原本会被传统选择剪枝丢弃的信号。 这些系统必须在严格的延迟和计算约束下运行。 SNNs本质上非常适合低延迟、低内存、实时推理，尤其是在现场可编程门阵列（FPGAs）上。 随着专用神经形态硬件的快速发展，预计会有进一步的提升。 使用CMS ADC2021数据集，我们设计并评估了一个简单的SNN自动编码器架构。 我们的结果表明，SNN自动编码器在所有信号模型中都与传统的自动编码器在LHC异常检测方面具有竞争力。 显示英文摘要

摘要： Anomaly detection offers a promising strategy for discovering new physics at the Large Hadron Collider (LHC). This paper investigates AutoEncoders built using neuromorphic Spiking Neural Networks (SNNs) for this purpose. One key application is at the trigger level, where anomaly detection tools could capture signals that would otherwise be discarded by conventional selection cuts. These systems must operate under strict latency and computational constraints. SNNs are inherently well-suited for low-latency, low-memory, real-time inference, particularly on Field-Programmable Gate Arrays (FPGAs). Further gains are expected with the rapid progress in dedicated neuromorphic hardware development. Using the CMS ADC2021 dataset, we design and evaluate a simple SNN AutoEncoder architecture. Our results show that the SNN AutoEncoders are competitive with conventional AutoEncoders for LHC anomaly detection across all signal models.