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

Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test