  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.
