# 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.

## Key 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.