Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test
This provides an efficient, anomaly-agnostic solution for data quality monitoring in particle physics experiments, though it appears incremental as it applies existing kernel methods to a specific domain.
The authors tackled real-time monitoring of particle detector data by developing a kernel method-based likelihood-ratio test to assess compatibility with reference data, demonstrating effectiveness on multivariate data from drift tube chamber muon detectors.
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.