A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators
This addresses operational inefficiencies in particle accelerators, representing an incremental improvement with specific gains.
The paper tackles the problem of beam interruptions in particle accelerators by forecasting interlock events using a novel time series classification approach, achieving an AUC of 0.71 and potentially reducing beam time loss by 0.5 seconds per interlock.
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also utilizes the advances of image classification techniques. Our best performing interlock-to-stable classifier reaches an Area under the ROC Curve value of $0.71 \pm 0.01$ compared to $0.65 \pm 0.01$ of a Random Forest model, and it can potentially reduce the beam time loss by $0.5 \pm 0.2$ seconds per interlock.