away or in lower density from other clusters of normal points, so they are expected to work better overall. The post-processing method following DBSCAN, represents an improvement as it reduces the number of false positives, whilst retaining the same TP and FN rates. The two examples in Fig. 9 are those where the classification was performed using a post-processing after DBSCAN. As a final remark, we would like to mention that ML Fig. 10. Outcome of the anomaly detection performed with SVM, DBSCAN, LOF, a binary OR between DBSCAN and LOF, and post-processing following DBSCAN methods. TP = True Positives (anomaly correctly detected), TN = True Negatives (normal point correctly detected), FP = False Positives, FN = False Negatives (from [36]). with β*=15 cm, Q' =15, and strong powering of the Landau octupoles, with no beam-beam effects. The right plot refers to the optics configuration of the LHC for the 2016 proton run at injection energy, Q' =8, and strong Landau octupoles to combat electron-cloud effects. It is rather uncommon that for a given angle the stable amplitude features considerable variations from seed to seed, so that its distribution over seeds features outliers. For our purposes, the outlier identification is carried out in steps [36]. Two types of ML approaches for the automatic detection of outliers have been tested. When using SL, the task of outlier detection is treated as a classification problem and a SVM is trained to distinguish between normal and abnormal points. Two UL approaches have also been considered, namely the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method [60] and the Local Outlier Factor (LOF) [61] algorithm. Fig. 10 reports the comparison between SVM, DBSCAN, and LOF algorithms. The labels predicted by DBSCAN and LOF were combined through a binary OR operation to create a fourth set of labels. A fifth set of labels is generated by detecting and removing false positives using a statistical method [36]. An iterative approach is performed with this post-processing, namely the algorithm starts at the minimum (maximum) point, and moves outwards (inwards), recalculating the statistical variables of the regular points at each step. The results indicate that the unsupervised methods have a performance an order of magnitude better than SVM in terms of false positives. However, they are worse in terms of false negatives, particularly when using LOF. The main explanation of this observation is in how the algorithm works. In supervised learning, the classes are assumed to be balanced between normal and abnormal points, as the algorithm tries to learn a decision boundary that separates the two sets of points. An imbalance induces a bias in the position of the decision boundary, so false positives are more likely. On the other hand, the unsupervised methods try to identify points further 54 approaches have recently been applied to the problem of improving the quality of the model describing the evolution of DA with time [62]. In this case, Gaussian processes have been very successfully used to generate synthetic points representing the DA at a given number of turns. These synthetic points have been added to those generated by the numerical simulations, and the whole data set has been used to determine the model describing the time evolution of the DA. In this way, a sizable improvement in the quality of the fit has been shown [62]. Conclusions and Outlook A selection of ML applications from a variety of domains linked with beam measurements for the CERN LHC have been presented and discussed in detail. In all applications, ML is seen as an efficient way to perform various types of classification processes on large data sets. One of the first LHC ML applications originated from the quest to improve optics measurement and correction by detecting faulty BPMs. Anomaly detection together with clustering techniques were instrumental to achieving successful data cleaning. This was followed by basic Neural Network implementation for optics correction which is already producing very promising results. Further improvements are anticipated using an autoencoder network for improving the quality of betatron-phase measurements and performing noise reduction on turn-by-turn data. For the automated alignment of collimators, excellent results have been obtained thanks to ML techniques which were used to discriminate between true beam-loss spikes and spurious events. This has resulted in a remarkable shortening of the setup time for the collimation system, with the ML implementation now fully operational. The next step is to move to the analysis of crosstalk effects between the loss signals of the two beams, as this could open the possibility to perform parallel collimator alignment with two circulating beams. Promising results have also been obtained by using a ML model for optimizing LHC beam lifetimes. However, rather than using only the operational data, this necessitated that the parameter space was properly explored by means of dedicated machine experiments. Although the ML-based model was found to describe operational configurations rather well, nonstandard configurations cannot be predicted, which indicates a certain lack of predictive power. Hence, exploring alternative ML approaches is a future line of research. Detection and classification of beam instabilities by means of Unsupervised Learning has been applied to the data collected by the LHC ObsBox system, with the goal of selecting interesting instability events from a huge collection of less IEEE Instrumentation & Measurement Magazine December 2021

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