Unsupervised Machine Learning for Detection of Faulty Beam Position Monitors
Elena Fol, Jaime Maria Coello de Portugal, Rogelio Tomás · International Linear Collider · 2019
Unsupervised learning includes anomaly detection techniques that are suitable for the detection of unusual events such as instrumentation faults in particle accelerators. In this work we present the application of decision trees-based algorithm to faulty BPMs detection at the LHC. This method achieves significant improvements in quality of optics measurements and allows to identify relevant signal properties that contribute to fault detection.