用机器学习优化超导加速器梯度，降低场致发射辐射。
Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac
- 基于带不确定性的机器学习预测多位置辐射水平
- 优化后中子与伽马辐射降低超40%
- 适合需要提升加速器安全性的实验物理团队
场发射会严重干扰超导射频直线加速器（linac）的运行。当腔体梯度提高时，加速器内部辐射水平可能呈指数增长，导致附近多个系统退化。本研究利用机器学习结合不确定性量化，预测linac内多个位置的辐射水平，并最终优化腔体梯度，在维持实验物理所需总能量增益的前提下，减少场发射引发的辐射。优化方案使中子和伽马辐射较标准运行设置降低超过40%。
原文摘要 · Abstract (English)
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.
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