Summary of Anomaly Detection Of Particle Orbit in Accelerator Using Lstm Deep Learning Technology, by Zhiyuan Chen et al.
Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology
by Zhiyuan Chen, Wei Lu, Radhika Bhong, Yimin Hu, Brian Freeman, Adam Carpenter
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Accelerator Physics (physics.acc-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This Machine Learning-based fault detection methodology develops an unsupervised approach to identify orbit lock anomalies and notify accelerator operations staff, enhancing the quality of the beam delivered to experimental halls. By using Long-Short Memory Networks (LSTM) Auto Encoder, the method captures normal patterns and predicts future values of monitoring sensors in the orbit lock system, detecting anomalies when the prediction error exceeds a threshold. The approach is tested on Jefferson Lab’s Continuous Electron Beam Accelerator Facility (CEBAF) data, achieving promising results with an accuracy range of 68.6%-89.3%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to catch mistakes in an important machine that helps scientists study tiny particles has been developed. This machine, called the electron accelerator, needs a special system to keep its “orbit” stable and controlled. When this system fails, people need to intervene to fix it. The new method uses computers to learn what normal patterns are and then detects when something goes wrong. It’s like having a smart alarm that can catch mistakes before they cause problems. This approach has been tested on real data from a lab called CEBAF and worked well, with an accuracy of around 80%. |
Keywords
* Artificial intelligence * Encoder * Lstm * Machine learning * Unsupervised
