Fast kernel methods for data quality monitoring as a goodness-of-fit test
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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 25, 2023
- Source ID
- 10.1088/2632-2153/acebb7
Entities
People
- Andrea Wulzer
- G. Grosso
- Jacopo Pazzini
- Lorenzo Rosasco
- Marco Letizia
- Marco Rando
- Marco Zanetti
- Nicolò Lai
Organizations
- Agencia Estatal de Investigación
- Air Force Office of Scientific Research
- Division of Computing and Communication Foundations
- Marie Skłodowska-Curie Actions