Automation of the LHC Collimator Beam-Based Alignment Procedure for Nominal Operation

The CERN Large Hadron Collider (LHC) is the largest particle accelerator in the world, built to accelerate and collide two counter-rotating beams. The LHC is susceptible to unavoidable beam losses, therefore a complex collimation system, made up of around 100 collimators, is installed in the LHC to protect its super-conducting magnets and sensitive equipment. The collimators are positioned around the beam following a multi-stage hierarchy. These settings are calculated following a beam-based alignment (BBA) technique, to determine the local beam position and beam size at each collimator. This procedure is currently semi-automated such that a collimation expert must continuously analyse the signal from the Beam Loss Monitoring (BLM) device positioned downstream of the collimator. Additionally, angular alignment are carried out to determine the most optimal angle for enhanced performance. The human element, in both the standard and angular BBA, is a major bottleneck in speeding up the alignment. This limits the frequency at which alignments can be performed to the bare minimum, therefore this dissertation seeks to improve the process by fully-automating the BBA. This work proposes to automate the human task of spike detection by using machine learning models. A data set was collated from previous alignment campaigns and fourteen manually engineered features were extracted. Six machine learning models were trained, analysed in-depth and thoroughly tested, obtaining a precision of over 95%. To automate the threshold selection task, data from previous alignment campaigns was analysed to define an algorithm to execute in real-time, as the threshold needs to be updated dynamically, corresponding to the changes in the beam losses. The thresholds selected by the algorithm were consistent with the user selections whereby all automatically selected thresholds were suitable selections. Finally, this work seeks to identify the losses generated by each collimator, such that any cross-talk across BLM devices is avoided. This involves building a cross-talk model to automate the parallel selection of collimators, and seeks to determine the actual beam loss signals generated by their corresponding collimators. Manual, expert control of the alignment procedure was replaced by these dedicated algorithms, such that the software was re-designed to achieve fully-automatic collimator alignments. This software is developed in a real-time environment, such that the fully-automatic BBA is implemented on top of the semi-automatic BBA, thus allowing for both alignment tools to be available together and maintaining backward-compatibility with all previous functionality. This new software was used for collimator alignments in 2018, for both standard and angular alignments. Automatically aligning the collimators decreased the alignment time by 70%, whilst maintaining the accuracy of the results. The work described in this dissertation was successfully adopted by CERN for LHC operation in 2018, and will continue to be used in the future as the default collimator alignment software for the LHC