mlsanityData to 13 Oct 2026

Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac

arXiv 2411.07018 ↗First posted 11 Nov 2024physics.acc-ph cs.LGcited by 1

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Abstract (arXiv)

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 9 of its 44 references that are in the map. First posted on arXiv 11 Nov 2024.

Builds on

9 of 44 references.

References · 9

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,430
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,902
  3. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 817
  4. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  5. 2020pymoo: Multi-objective Optimization in Pythoncited by 197
  6. 2021Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantificationcited by 57
  7. 2020Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratorycited by 15
  8. 2024Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratorycited by 1
  9. 2024A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFELcited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Cohort Organized Learning: Clustering Through Agreementcited by 0