用生成模型学习激光脉冲形状与电子束质量的映射关系，提升优化效率。
Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems
- 基于Wasserstein自编码器构建可微分的隐空间接口，连接脉冲形状与束流动力学。
- 隐空间连续且可解释，线性插值可平滑过渡不同脉冲类型，重建精度高。
- 模型从仿真推广到真实实验数据，减少对昂贵模拟的依赖，适合加速束流优化。
在自由电子激光器光注入器中控制纵向激光脉冲形状是优化电子束质量的有效手段，但大规模设计空间的系统探索受限于暴力脉冲传播模拟的成本。本文提出一种基于Wasserstein自编码器的生成建模框架，学习脉冲整形与下游束流动力学之间的可微分隐空间接口。实验结果表明，所学隐空间具有连续性和可解释性，同时保持高保真度重建。如高阶高斯脉冲族在隐空间中呈现连贯轨迹，标准化时间长度后其隐表示与脉冲能量相关。通过主成分分析和高斯混合模型分析，揭示了具有良好结构的隐空间几何，支持不同脉冲类型间的线性插值。模型从仿真数据泛化至真实实验脉冲测量，能准确重建并一致嵌入脉冲至学习流形。总体上，该方法降低对昂贵脉冲传播模拟的依赖，促进后续束流动力学模拟与分析。
原文摘要 · Abstract (English)
Controlling the longitudinal laser pulse shape in photoinjectors of Free-Electron Lasers is a powerful lever for optimizing electron beam quality, but systematic exploration of the vast design space is limited by the cost of brute-force pulse propagation simulations. We present a generative modeling framework based on Wasserstein Autoencoders to learn a differentiable latent interface between pulse shaping and downstream beam dynamics. Our empirical findings show that the learned latent space is continuous and interpretable while maintaining high-fidelity reconstructions. Pulse families such as higher-order Gaussians trace coherent trajectories, while standardizing the temporal pulse lengths shows a latent organization correlated with pulse energy. Analysis via principal components and Gaussian Mixture Models reveals a well behaved latent geometry, enabling smooth transitions between distinct pulse types via linear interpolation. The model generalizes from simulated data to real experimental pulse measurements, accurately reconstructing pulses and embedding them consistently into the learned manifold. Overall, the approach reduces reliance on expensive pulse-propagation simulations and facilitates downstream beam dynamics simulation and analysis.
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