Fast kernel methods for data quality monitoring as a goodness-of-fit test

• DOI: 10.1088/2632-2153/acebb7
• Publication Date: 2023-07-28T22:40:27Z
• Authors: Gaia Grosso; Nicolò Lai; Marco Letizia; Jacopo Pazzini; Marco Rando; Lorenzo Rosasco; Andrea Wulzer; Marco Zanetti
• Keywords: Kernel (algebra); Goodness of fit

Abstract We propose an accurate and efficient 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 behaviour under normal circumstances, via likelihood-ratio hypothesis test. model based on modern implementation kernel methods, nonparametric algorithms that can learn any continuous function given enough data. resulting agnostic type anomaly may be present Our study demonstrates effectiveness this strategy multivariate from drift tube chamber muon detectors.