机器学习应用于XFEL中的单次激发X射线诊断
Machine learning applied to single-shot x-ray diagnostics in an XFEL
摘要：
X射线自由电子激光（XFEL）是目前唯一能够产生明亮的几fs脉冲的光源，其光子能量从100 eV到10 keV以上。由于随机SASE操作原理和其他技术问题，输出脉冲受到大的波动，使得有必要表征每次发射的X射线脉冲以用于数据分类目的。我们提出了一种技术，应用机器学习工具来预测X射线脉冲特性，使用简单的电子束和X射线参数作为输入。使用这种技术在直线加速器相干光源（LCLS），我们报告的平均误差低于0.3 eV的预测在530 eV的光子能量和低于1.6 fs的预测之间的延迟两个X射线脉冲。我们还证明了光谱形状预测的平均一致率为97%。这种方法可能被用于下一代高重复率XFEL，以提供在全重复率的复杂X射线脉冲的准确知识。
英文摘要：
X-ray free-electron lasers (XFELs) are the only sources currently able to produce bright few-fs pulses with tunable photon energies from 100 eV to more than 10 keV. Due to the stochastic SASE operating principles and other technical issues the output pulses are subject to large fluctuations, making it necessary to characterize the x-ray pulses on every shot for data sorting purposes. We present a technique that applies machine learning tools to predict x-ray pulse properties using simple electron beam and x-ray parameters as input. Using this technique at the Linac Coherent Light Source (LCLS), we report mean errors below 0.3 eV for the prediction of the photon energy at 530 eV and below 1.6 fs for the prediction of the delay between two x-ray pulses. We also demonstrate spectral shape prediction with a mean agreement of 97%. This approach could potentially be used at the next generation of high-repetition-rate XFELs to provide accurate knowledge of complex x-ray pulses at the full repetition rate.
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