Automatic spike detection in beam loss signals for LHC collimator alignment

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.