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.

翻译：电子束加速器在许多科学与技术领域中至关重要。其运行高度依赖于电子束的稳定性与精确性。传统诊断技术在应对电子束复杂动态特性方面面临困难。尤其在自由电子激光（FEL）场景中，从根本上无法对单个电子束团测量其激光开启与关闭状态下的电子功率分布。这是实现光子脉冲轮廓精确重构的关键障碍。为突破此障碍，我们开发了一种机器学习模型，该模型利用激光开启时可获取的机器参数，预测电子束团在激光关闭状态下的时间功率分布。该模型经过统计验证，与最先进的批量校准方法相比展现出更优的预测性能。本研究提出的方法是虚拟脉冲重构诊断（VPRD）工具的核心组成部分，该工具旨在无需在激光关闭状态下进行重复测量即可重构单个光子脉冲的功率分布。这有望显著提升自由电子激光装置的整体诊断能力。