Detection of faulty beam position monitors using unsupervised learning

Optics measurements at the LHC are mainly based on turn-by-turn signal from hundreds of beam position monitors (BPMs). Faulty BPMs produce erroneous signal causing unreliable computation of optics functions. Therefore, detection of faulty BPMs prior to optics computation is crucial for adequate optics analysis. Most of the faults can be removed by applying traditional cleaning techniques. However, optics functions reconstructed from the cleaned turn-by-turn data systematically exhibit a few nonphysical values which indicate the presence of remaining faulty BPMs. A novel method based on the Isolation Forest algorithm has been developed and applied in LHC operation, allowing to significantly reduce the number of undetected faulty BPMs, thus improving the optics measurements. This report summarizes the operational results and discusses the evaluation of the developed method on simulations, including extensive studies and optimization of the preexisting cleaning technique and verification of a new method in terms of coupling measurement. The advantages of the chosen algorithm compared to some other unsupervised learning techniques are also discussed.