Anomaly detection with spiking neural networks for LHC physics

Abstract Anomaly detection offers a promising strategy for discovering new physics at the Large Hadron Collider (LHC). This paper investigates autoencoders (AEs) 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. 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 AE architecture. Our results show that the SNN AEs are competitive with conventional AEs for LHC anomaly detection across all signal models.