We here propose a machine learning approach for monitoring particle detectors in real-time. The goal is to assess the compatibility of incoming experimental data with a reference dataset, characterising the data behaviour under normal circumstances, via a likelihood-ratio hypothesis test. The model is based on a modern implementation of kernel methods, nonparametric algorithms that can learn any continuous function given enough data. The resulting approach is efficient and agnostic to the type of anomaly that may be present in the data. Our study demonstrates the effectiveness of this strategy on multivariate data from drift tube chamber muon detectors.

翻译：本文提出了一种用于实时监测粒子探测器的机器学习方法。目标是通过似然比假设检验，评估新输入的实验数据与表征正常数据行为的参考数据集的兼容性。该模型基于现代核方法实现——一种在数据充足条件下能学习任意连续函数的非参数算法。该方法高效且对数据中可能存在的异常类型保持不可知性。本研究通过漂移管室μ子探测器的多变量数据验证了该策略的有效性。