  We present a machine learning based approach for real-time monitoring of
particle detectors. The proposed strategy evaluates the compatibility between
incoming batches of experimental data and a reference sample representing the
data behavior in normal conditions by implementing a likelihood-ratio
hypothesis test. The core model is powered by recent large-scale
implementations of kernel methods, nonparametric learning algorithms that can
approximate any continuous function given enough data. The resulting algorithm
is fast, efficient and agnostic about the type of potential anomaly in the
data. We show the performance of the model on multivariate data from a drift
tube chambers muon detector.
