A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Abstract

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.

References in corpus (3)

• Recurrence Plots for the Analysis of Complex Systems
• Neural Networks for Modeling and Control of Particle Accelerators
• Machine learning for beam dynamics studies at the CERN Large Hadron Collider

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• Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators
• Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators
• Data-driven modeling of beam loss in the LHC
• Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning