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：我们在此提出实时监测粒子探测器的机器学习方法。 目的是通过概率差假设测试,评估进取的实验数据与参考数据集的兼容性,说明正常情况下的数据行为。 模型基于现代实施内核方法、非参数算法,可以学习任何连续功能,并有足够的数据。 由此产生的方法对数据中可能存在的异常类型是高效和不可知的。 我们的研究显示,这一战略对流管室粘膜探测器的多变量数据是有效的。</s>