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Table of Contents


NameInstitutionMail AddressSocial Contacts
Gaia GrossoINFN Padova, University of Padova, CERNgaia.grosso@cern.chN/A
Nicolò LaiUniversity of Padovanicolo.lai@studenti.unipd.itN/A
Marco LetiziaMaLGa-DIBRIS, Università di Genvoa e INFN, Sezione di Genovamarco.letizia@edu.unige.itN/A
Jacopo PazziniINFN Padova, University of Padovajacopo.pazzini@unipd.itN/A
Marco RandoMaLGa-DIBRIS, Università di Genvoamarco.rando@edu.unige.itN/A
Lorenzo RosascoMaLGa-DIBRIS, Università di Genvoa, IIT Genova, MITLorenzo.Rosasco@unige.itN/A
Andrea WulzerIFAE, N/A
Marco ZanettiINFN Padova, University of N/A

How to Obtain Support

General Information

ML/DL TechnologiesClassification, Logistic Regression, Kernel Methods
Science FieldsHigh Energy Physics



Fully annotated, runnable

Software and Tools

Programming LanguagePython
ML Toolset


Additional librariesFalkon
Suggested EnvironmentsPython virtual environment available at main repository

Needed datasets

Data Creator
Data TypeReal acquisition
Data Size12 MB
Data Source

Short Description of the Use Case

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.

How to execute it

Python environment, code and a fully annotated Jupyter notebook are available at


G. Grosso, N. Lai, M. Letizia, J. Pazzini, M. Rando, L. Rosasco, A. Wulzer, M. Zanetti, Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test,[2303.05413].