Accurate characterization of an attosecond pulse from streaking trace is an indispensable step in studying the ultrafast electron dynamics on the attosecond scale. Conventional attosecond pulse retrieval methods face two major challenges: the ability to incorporate a complete physics model of the streaking process, and the ability to model the uncertainty of pulse reconstruction in the presence of noise. Here we propose a pulse retrieval method based on conditional variational generative network (CVGN) that can address both demands. Instead of learning the inverse mapping from a streaking trace to a pulse profile, the CVGN models the distribution of the pulse profile conditioned on a given streaking trace measurement, and is thus capable of assessing the uncertainty of the retrieved pulses. This capability is highly desirable for low-photon level measurement, which is typical in attosecond streaking experiments in the water window X-ray range. In addition, the proposed scheme incorporates a refined physics model that considers the Coulomb-laser coupling and photoelectron angular distribution in streaking trace generation. CVGN pulse retrievals under various simulated noise levels and experimental measurement have been demonstrated. The results showed high pulse reconstruction consistency for streaking traces when peak signal-to-noise ratio (SNR) exceeds 6, which could serve as a reference for future learning-based attosecond pulse retrieval.

从条纹轨迹中准确表征阿秒脉冲是研究阿秒尺度超快电子动力学不可或缺的一步。传统的阿秒脉冲检索方法面临两个主要挑战：整合条纹过程的完整物理模型的能力，以及在存在噪声的情况下对脉冲重建的不确定性进行建模的能力。在这里，我们提出了一种基于条件变分生成网络（CVGN）的脉冲检索方法，可以满足这两个需求。 CVGN 不是学习从裸奔轨迹到脉冲轮廓的逆映射，而是对给定裸奔轨迹测量条件下的脉冲轮廓的分布进行建模，因此能够评估检索到的脉冲的不确定性。这种能力对于低光子水平测量是非常理想的，这在水窗 X 射线范围内的阿秒条纹实验中是典型的。此外，所提出的方案还结合了一个精细的物理模型，该模型考虑了裸奔痕迹生成中的库仑-激光耦合和光电子角分布。已经证明了在各种模拟噪声水平和实验测量下的 CVGN 脉冲检索。结果表明，当峰值信噪比（SNR）超过6时，裸奔痕迹的脉冲重建一致性较高，可为未来基于学习的阿秒脉冲反演提供参考。