X-ray free-electron lasers (XFELs) as the world's brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.

翻译：X射线自由电子激光器(XFELs)是世界最亮的光源,它提供了超短X光脉冲,其持续时间通常以femtocons为依次。最近,它们接近并进入了二进制,这为单分子成像和研究非线性及超快现象,如局部电子动态带来了新的承诺。XFELs的技术演化为控制良好的超快过程精确计量仪光源,但受到以下因素的阻碍:二边边界X光脉冲特征化的诊断能力,在这方面,光电子脉冲的光谱谱谱谱学技术成功地证明如何在单发的基础上不毁灭地恢复XFEL脉冲的确切时间能量结构。通过使用人工智能技术,特别是革命性神经网络,我们在这里展示了如何利用这一技术从其校准阶段,甚至在高重复率XFELs进行常规诊断,从而在所有相关学科中加强和改进其科学无障碍性。