Deep Learning Reconstruction of Ultra-Short Pulses

TL;DR

This paper introduces a novel deep neural network method for reconstructing ultra-short laser pulses, enhancing the ability to characterize extremely brief and weak pulses crucial for ultrafast science.

Contribution

It is the first to apply deep learning for the reconstruction of ultra-short optical pulses, both numerically and experimentally.

Findings

Successful numerical and experimental demonstration

Extended range of pulse characterization including weak attosecond pulses

Potential to improve ultrafast science diagnostics

Abstract

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.