用机器学习预测自由电子激光单束电子脉冲功率，突破无法直接测量的瓶颈。
Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power
- 基于激光开启时的机器参数，用机器学习推断关闭时的电子功率分布
- 模型预测精度优于现有批量校准方法，实现单次测量重建
- 适合需要高精度光脉冲诊断的自由电子激光用户
电子束加速器在众多科学与技术领域中至关重要，其运行依赖于电子束的稳定性与精度。传统诊断手段难以应对电子束复杂多变的特性。尤其在自由电子激光（FEL）中，无法对单个电子束流的激光开启与关闭状态下的电子功率分布进行直接测量，这成为精确重构光子脉冲轮廓的关键障碍。为此，我们开发了一种机器学习模型，利用激光开启时可获取的机器参数，预测激光关闭状态下的电子束时间功率分布。该模型经统计验证，预测性能优于当前最先进的批量校准方法。本文工作是虚拟脉冲重构诊断（VPRD）工具的核心，旨在无需重复进行激光关闭状态测量即可重构单个光子脉冲的功率轮廓，有望显著提升大型FEL装置的诊断能力。
原文摘要 · Abstract (English)
Electron beam accelerators are essential in many scientific and technological fields. Their operation relies heavily on the stability and precision of the electron beam. Traditional diagnostic techniques encounter difficulties in addressing the complex and dynamic nature of electron beams. Particularly in the context of free-electron lasers (FELs), it is fundamentally impossible to measure the lasing-on and lasingoff electron power profiles for a single electron bunch. This is a crucial hurdle in the exact reconstruction of the photon pulse profile. To overcome this hurdle, we developed a machine learning model that predicts the temporal power profile of the electron bunch in the lasing-off regime using machine parameters that can be obtained when lasing is on. The model was statistically validated and showed superior predictions compared to the state-of-the-art batch calibrations. The work we present here is a critical element for a virtual pulse reconstruction diagnostic (VPRD) tool designed to reconstruct the power profile of individual photon pulses without requiring repeated measurements in the lasing-off regime. This promises to significantly enhance the diagnostic capabilities in FELs at large.
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