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.

中文翻译：

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使用无监督学习来检测故障光束位置监控器

大型强子对撞机的光学测量主要基于来自数百个光束位置监控器（BPM）的转向信号。错误的BPM会产生错误的信号，从而导致光学功能的计算不可靠。因此，在进行光学计算之前检测出故障的BPM对于充分的光学分析至关重要。大多数故障可以通过应用传统的清洁技术来消除。但是，从清洗的逐行扫描数据重建的光学功能系统地显示出一些非物理值，表明存在剩余的故障BPM。已开发出一种基于隔离森林算法的新颖方法，并将其应用于LHC操作中，从而可以显着减少未检测到的故障BPM的数量，从而改善光学测量。本报告总结了操作结果，并讨论了对已开发方法的仿真评估，包括对现有清洁技术的广泛研究和优化，以及在耦合测量方面对新方法的验证。还讨论了所选算法与其他一些无监督学习技术相比的优势。