Attosecond pulse retrieval from noisy streaking traces with conditional variational generative network.

Area of Science:

• Ultrafast Science
• Attosecond Physics
• Quantum Dynamics

Background:

• Accurate attosecond pulse characterization is vital for studying ultrafast electron dynamics.
• Conventional methods struggle with complete physics modeling and noise-induced uncertainty in pulse reconstruction.
• Attosecond streaking experiments, especially in the water window, often involve low photon counts.

Purpose of the Study:

• To propose a novel pulse retrieval method addressing limitations of conventional techniques.
• To develop a method capable of assessing the uncertainty of reconstructed attosecond pulses.
• To incorporate a refined physics model into the pulse retrieval process.

Main Methods:

• Utilizing a conditional variational generative network (CVGN) to model pulse profile distributions conditioned on streaking trace measurements.
• Implementing a refined physics model that includes Coulomb-laser coupling and photoelectron angular distribution.
• Testing the CVGN method under various simulated noise levels and experimental conditions.

Main Results:

• The CVGN method successfully models the distribution of pulse profiles conditioned on streaking trace data.
• The capability to assess pulse reconstruction uncertainty is demonstrated, beneficial for low-photon measurements.
• High pulse reconstruction consistency was achieved for streaking traces with a peak signal-to-noise ratio (SNR) above 6.

Conclusions:

• The proposed CVGN method offers a robust solution for attosecond pulse characterization in streaking experiments.
• The ability to quantify reconstruction uncertainty is a significant advantage, particularly for low-light conditions.
• The demonstrated performance provides a benchmark for future learning-based attosecond pulse retrieval techniques.