Femtosecond pulse compression using a neural-network algorithm

被引：27

作者：

Farfan, Camille A. ^{[1
]}

Epstein, Jordan ^{[1
]}

Turner, Daniel B. ^{[1
]}

机构：

[1] NYU, Dept Chem, New York, NY 10003 USA

来源：

OPTICS LETTERS | 2018年 / 43卷 / 20期

基金：

美国国家科学基金会;

关键词：

GENERATION;

D O I：

10.1364/OL.43.005166

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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 100x faster than a standard evolutionary algorithm. (C) 2018 Optical Society of America

引用

收藏

页码：5166 / 5169

页数：4

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