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首页> 外文期刊>Nuclear Instruments & Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment >Automatic spike detection in beam loss signals for LHC collimator alignment
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Automatic spike detection in beam loss signals for LHC collimator alignment

机译：用于LHC准直器对齐的光束损耗信号中的自动尖峰检测

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摘要

A collimation system is installed in the Large Hadron Collider to protect its super-conducting magnets and sensitive equipment from potentially dangerous beam halo particles. The collimator settings are determined following an alignment procedure, whereby collimator jaws are moved towards the beam until a suitable spike pattern, consisting of a sharp rise followed by a slow decay, is observed in nearby beam loss monitors. This indicates the collimator jaw is aligned to the beam. The current method for aligning collimators is semi-automated whereby an operator must continuously observe the loss signals to determine whether the jaw has touched the beam, or if some other perturbation in the beam caused the losses. The human element in this procedure can result in errors and is a major bottleneck in automating and speeding up the alignment. This paper proposes to automate the human task of spike detection by using machine learning. A data set was formed from previous alignment campaigns, from which fourteen manually engineered features were extracted and six machine learning models were trained, analysed in-depth and thoroughly tested. The suitability of using machine learning in LHC operation was confirmed during collimator alignments performed in 2018, which significantly benefited from the models trained through machine learning in this study.
机译：准直系统安装在大型强子集中器中，以保护其超级导电磁铁和敏感设备，从潜在的危险梁晕泡颗粒。在对准过程之后确定准直器设置，由此准直器钳口朝向梁移动，直到附近的光束损耗监视器中由急剧上升组成的合适的尖峰图案。这表示准直器钳口与光束对齐。用于对准准直器的电流方法是半自动的，由此操作员必须连续地观察损耗信号以确定钳口是否触摸了光束，或者如果光束中的其他一些扰动导致损失导致损失。该过程中的人体元素可能导致错误，并且是自动化和加速对准的主要瓶颈。本文建议通过使用机器学习自动化尖峰检测的人工任务。数据集由先前的对齐运动形成，从中提取了14个手动工程化特征，培训了六种机器学习模型，深入分析并彻底测试。在2018年执行的准直器对准期间确认了在LHC操作中使用机器学习的适用性，这从本研究中通过机器学习训练的模型显着受益。

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