mlsanityData to 7 Oct 2026

Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

arXiv 2006.06562 ↗First posted 11 Jun 2020physics.acc-ph cs.LGcited by 15

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Abstract (arXiv)

We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the 8 cavities in the cryomodule. Subject matter experts (SME) are able to analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time (rather than post-mortem) identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the ML models during a recent physics run. Results show the cavity identification and fault classification models have accuracies of 84.9% and 78.2%, respectively.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2020.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 5; 2025: 0; 2026: 4.

15 papers.

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  1. 2021High-fidelity Prediction of Megapixel Longitudinal Phase-space Images of Electron Beams using Encoder-Decoder Neural Networkscited by 7
  2. 2024A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle acceleratorscited by 7
  3. 2021Uncertainty aware anomaly detection to predict errant beam pulses in the SNS acceleratorcited by 5
  4. 2021Machine Learning in Nuclear Physicscited by 4
  5. 2023Robust Errant Beam Prognostics with Conditional Modeling for Particle Acceleratorscited by 4
  6. 2022Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavitiescited by 3
  7. 2024Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratorycited by 1
  8. 2024A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFELcited by 1
  9. 2024Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linaccited by 1
  10. 2023Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regressioncited by 0
  11. 2024Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technologycited by 0
  12. 2026Toward a Fully Autonomous, AI-Native Particle Acceleratorcited by 0
3 more
  1. 2026Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problemscited by 0
  2. 2026Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Sourcecited by 0
  3. 2026Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructurecited by 0