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

TL;DR

This paper introduces a novel CNN-based method using Recurrence Plots for classifying and forecasting interlock events in particle accelerators, aiming to reduce beam time loss.

Contribution

It presents a new approach combining Recurrence Plots and CNNs for time series forecasting in particle accelerators, improving prediction accuracy over traditional models.

Findings

Achieved AUC of 0.71 for interlock prediction

Potential to reduce beam time loss by 0.5 seconds per event

Outperformed Random Forest baseline

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.