Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test

2023/03/09 by G. Grosso, Nicolò Lai, Grosso, Gaia +13 · 4 citations
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #High Energy Physics - Experiment (hep-ex) #Machine Learning (cs.LG) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2303.05413

openalex publication_date 2023/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

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.

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• End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment
• Learning to Validate Generative Models: a Goodness-of-Fit Approach
• Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies
• Comparing Generative Models with the New Physics Learning Machine

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