Modeling of large-scale research facilities is extremely challenging due to complex physical processes and engineering problems. Here, we adopt a data-driven approach to model the longitudinal phase-space-diagnostic beamline at the photoinector of the European XFEL with an encoder-decoder neural-network model. A deep convolutional neural network (decoder) is used to build images, measured on the screen, from a small feature map generated by another neural network (encoder). We demonstrate that the model, trained only with experimental data, can make high-fidelity predictions of megapixel images for the longitudinal phase-space measurement without any prior knowledge of photoinjectors or electron beams. The prediction significantly outperforms existing methods. We also show the scalability and interpretability of the model by sharing the same decoder with more than one encoder, used for different setups of the photoinjector, and propose a pragmatic way to model a facility with various diagnostics and working points. This opens the door to a way of accurately modeling a photoinjector using neural networks and experimental data. The approach can possibly be extended to the whole accelerator and even other types of scientific facility.

中文翻译：

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使用编码器-解码器神经网络对电子束的百万像素纵向相空间图像进行高保真预测

由于复杂的物理过程和工程问题，大型研究设施的建模极具挑战性。在这里，我们采用数据驱动的方法，使用编码器-解码器神经网络模型对欧洲 XFEL 光电感应器处的纵向相空间诊断光束线进行建模。深度卷积神经网络（解码器）用于根据另一个神经网络（编码器）生成的小特征图构建在屏幕上测量的图像。我们证明，仅使用实验数据训练的模型可以对用于纵向相空间测量的百万像素图像进行高保真预测，而无需任何光注射器或电子束的先验知识。该预测明显优于现有方法。我们还通过与多个编码器共享相同的解码器来展示模型的可扩展性和可解释性，用于光注射器的不同设置，并提出了一种实用的方法来对具有各种诊断和工作点的设施进行建模。这为使用神经网络和实验数据准确建模光注射器打开了大门。该方法可能会扩展到整个加速器甚至其他类型的科学设施。