Deep learning reconstruction of ultrashort pulses

Tom Zahavy, Alex Dikopoltsev*, Daniel Moss, Gil Ilan Haham, Oren Cohen, Shie Mannor, Mordechai Segev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

Ultrashort laser pulses with femtosecond to attosecond pulse duration are the shortest systematic events humans can currently create. Characterization (amplitude and phase) of these pulses is a crucial ingredient in ultrafast science, e.g., exploring chemical reactions and electronic phase transitions. Here, we propose and demonstrate, numerically and experimentally, what is to the best of our knowledge, the first deep neural network technique to reconstruct ultrashort optical pulses. Employing deep neural networks for reconstruction of ultrashort pulses enables diagnostics of very weak pulses and offers new possibilities, e.g., reconstruction of pulses using measurement devices without knowing in advance the relations between the pulses and the measured signals. Finally, we demonstrate the ability to reconstruct ultrashort pulses from their experimentally measured frequency-resolved optical gating traces via deep networks that have been trained on simulated data.

Original languageEnglish
Pages (from-to)666-673
Number of pages8
JournalOptica
Volume5
Issue number5
DOIs
StatePublished - 20 May 2018
Externally publishedYes

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