利用机器学习从X射线自由电子激光器中高效预测阿秒双色脉冲
Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning
AI总结：
本研究采用神经网络和决策树等机器学习方法，利用单次发射参数预测X射线自由电子激光器产生的阿秒双色脉冲的中心光子能量，提升了光谱测量效率并预期改善时间分辨X射线光谱实验结果。
摘要：
X射线自由电子激光器是产生相干、高强度X射线的光源，在超快测量和动态结构成像中有广泛应用。由于自放大自发辐射过程的随机性以及控制电子注入的困难，输出脉冲表现出显著的噪声和有限的时间相干性。用于表征双色X射线脉冲的标准测量技术面临挑战，因为它们要么是侵入性的，要么诊断成本高昂。在本工作中，我们采用神经网络和决策树等机器学习方法，利用在单次发射的高重复频率下容易记录的参数，预测阿秒基频和二次谐波脉冲对的中心光子能量。利用真实实验数据，我们在优化机器学习方法训练时间的同时，对输入参数进行了详细的特征分析。我们的预测模型能够在不测量其中一个脉冲的情况下预测另一个脉冲的中心光子能量，从而在不扩展光谱仪检测窗口的情况下充分利用光谱仪。我们预期这些方法将应用于使用XFEL的X射线光谱学中，例如时间分辨X射线吸收和光电子能谱，在这些领域中，输入光谱测量的改进将带来更好的实验结果。
英文摘要：
X-ray free-electron lasers are sources of coherent, high-intensity X-rays with numerous applications in ultra-fast measurements and dynamic structural imaging. Due to the stochastic nature of the self-amplified spontaneous emission process and the difficulty in controlling injection of electrons, output pulses exhibit significant noise and limited temporal coherence. Standard measurement techniques used for characterizing two-coloured X-ray pulses are challenging, as they are either invasive or diagnostically expensive. In this work, we employ machine learning methods such as neural networks and decision trees to predict the central photon energies of pairs of attosecond fundamental and second harmonic pulses using parameters that are easily recorded at the high-repetition rate of a single shot. Using real experimental data, we apply a detailed feature analysis on the input parameters while optimizing the training time of the machine learning methods. Our predictive models are able to make predictions of central photon energy for one of the pulses without measuring the other pulse, thereby leveraging the use of the spectrometer without having to extend its detection window. We anticipate applications in X-ray spectroscopy using XFELs, such as in time-resolved X-ray absorption and photoemission spectroscopy, where improved measurement of input spectra will lead to better experimental outcomes.
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