Femtosecond pulse compression using a neural-network algorithm

Camille A. Farfan, Jordan Epstein, Daniel B. Turner

Research output: Contribution to journalArticlepeer-review

Abstract

A key requirement for femtosecond spectroscopy measurements is to compress the laser pulse to its transform-limited duration. In particular, for few-cycle laser pulses, the compression process is time-consuming using conventional algorithms that converge statistically. Here we show that machine learning can accelerate the process of pulse compression: we have developed an adaptive neural-network algorithm to control a deformable-mirror-based pulse shaper that converges 100× faster than a standard evolutionary algorithm.

Original languageEnglish (US)
Pages (from-to)5166-5169
Number of pages4
JournalOptics Letters
Volume43
Issue number20
DOIs
StatePublished - Oct 15 2018

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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