Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, particle accelerators can fault and abort operations for numerous reasons. These faults impact the availability of particle accelerators during scheduled run-time and hamper the efficiency and the overall science output. To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability of particle accelerators. Semi-supervised Machine Learning (ML) based anomaly detection approaches such as autoencoders and variational autoencoders are often used for such tasks. However, supervised ML techniques such as Siamese Neural Network (SNN) models can outperform unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data's variability due to system configuration changes. To address this challenge, we employ Conditional Siamese Neural Network (CSNN) models and Conditional Variational Auto Encoder (CVAE) models to predict errant beam pulses at the Spallation Neutron Source (SNS) under different system configuration conditions and compare their performance. We demonstrate that CSNN outperforms CVAE in our application.

翻译：粒子加速器结构复杂，包含数千个组件，其中许多设备运行在峰值功率状态。因此，粒子加速器可能因多种原因发生故障并中断运行。这些故障会降低加速器在计划运行时间内的可用性，影响运行效率及整体科学产出。为预防此类故障，我们应用异常检测技术预测异常行为，并通过预判性操作提升加速器总可用性。基于半监督机器学习的异常检测方法（如自编码器与变分自编码器）常被用于此类任务。然而，通过利用标签信息，基于监督学习的孪生神经网络模型在异常检测中能够超越无监督或半监督方法。针对粒子加速器异常检测的特殊挑战——系统配置变化导致的数据变异性，我们采用条件孪生神经网络与条件变分自编码器模型，预测散裂中子源在不同系统配置条件下的故障束流脉冲，并对比其性能。实验表明，CSNN在本应用场景中的性能优于CVAE。