Sep 12 – 16, 2022
OAC conference center, Kolymbari, Crete, Greece.
Europe/Athens timezone

A flexible and efficient machine learning approach for data quality monitoring

Sep 14, 2022, 7:05 PM
10m
OAC conference center, Kolymbari, Crete, Greece.

OAC conference center, Kolymbari, Crete, Greece.

Poster Particle Physics Wine Tasting and Poster Session

Speaker

Dr Marco Letizia

Description

We present a machine learning approach for real-time detector monitoring. The corresponding core algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. The model evaluates the compatibility between incoming batches of experimental data and a reference data sample, by implementing a hypothesis testing procedure based on the likelihood ratio. The resulting model is fast, efficient and agnostic about the type of potential anomaly in the data. We show the performance of the model on multivariate data from muon chamber monitoring.

Primary authors

Andrea Wulzer (CERN and EPFL) Gaia Grosso (Universita e INFN, Padova (IT)) Jacopo Pazzini (Università e INFN, Padova (IT)) Dr Marco Letizia Marco Rando (Universita` degli Studi di Genova) Marco Zanetti (Universita e INFN, Padova (IT)) Mr Nicolò Lai (Università di Padova)

Presentation materials