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The 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 human task of spike classification can be automated by training a pattern recognition model to automatically classify between alignment and non-alignment spikes. A data set was collated 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 [1].
[1] G. Azzopardi et al., "Automatic spike detection in beam loss signals for LHC collimator alignment," in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 934, pp. 10-18, 2019.
