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

arXiv: https://arxiv.org/abs/2102.00786 · HTML: https://mlsanity.com/paper/2102.00786/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)

## Built on by (most cited first, 7 of 7)

- [Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator](https://mlsanity.com/paper/2110.12006/) (2021)
- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators](https://mlsanity.com/paper/2312.10040/) (2023)
- [Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning](https://mlsanity.com/paper/2107.06207/) (2021)
- [Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators](https://mlsanity.com/paper/2209.10705/) (2022)
- [Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory](https://mlsanity.com/paper/2404.15829/) (2024)
- [Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression](https://mlsanity.com/paper/2303.08984/) (2023)

## Abstract (arXiv)

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 ± 0.01 compared to 0.65 ± 0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5 ± 0.2 seconds per interlock.