Instrumentation & Measurement Magazine 24-9 - 55

interesting data. The combination of anomaly-detection
techniques and Hierarchical Clustering algorithm has led
to promising result. The possibility to apply such an ML approach
to the actual automatic ObsBox triggering will now be
explored, to reduce the amount of uninteresting data saved to
file.
Possible heating issues during LHC operation can be identified
by ML techniques through the classification of vacuum
gauge measurements. Deep learning approaches are under
investigation to provide a boost in the performance of classification
algorithms following the promising initial results
achieved. The ultimate goal is the deployment of this approach
when operation will be resumed for the LHC Run 3.
Considering the data obtained from numerical simulation
as a testbench for ML techniques has also been discussed,
with the case considered that of determining outliers in the
DA distribution for different configurations of magnetic field
errors. An efficient approach has been devised based on Unsupervised

Instrumentation & Measurement Magazine 24-9

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 24-9

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