Deep Learning Reconstruction of Ultra-Short Pulses
This addresses a key bottleneck in ultrafast science for researchers, but it is incremental as it applies an existing deep learning method to a new domain.
The paper tackles the problem of characterizing ultra-short laser pulses by proposing and demonstrating the first deep neural network technique for reconstruction, showing potential to extend the range of pulses that can be characterized, such as enabling diagnosis of very weak attosecond pulses.
Ultra-short laser pulses with femtosecond to attosecond pulse duration are the shortest systematic events humans can create. Characterization (amplitude and phase) of these pulses is a key ingredient in ultrafast science, e.g., exploring chemical reactions and electronic phase transitions. Here, we propose and demonstrate, numerically and experimentally, the first deep neural network technique to reconstruct ultra-short optical pulses. We anticipate that this approach will extend the range of ultrashort laser pulses that can be characterized, e.g., enabling to diagnose very weak attosecond pulses.