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Proceedings Paper

Ultrashort-pulse retrieval using frequency-resolved optical gating and an artificial neural network
Author(s): Marco A. Krumbuegel; Kenneth W. DeLong; David N. Fittinghoff; John N. Sweetser; Rick P. Trebino
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Paper Abstract

Frequency-resolved optical gating (FROG) is a technique that allows the determination of the intensity and phase of ultrashort laser pulses. In FROG, a spectrogram of the pulse, the so- called FROG trace, is produced, from which the intensity and phase is then retrieved using an iterative algorithm. This algorithm performs well for all types of pulses, but it sometimes requires more than a minute to converge, and more rapid retrieval is important for many applications. It is therefore desirable to have a non-iterative computational method capable of inverting the function that relates the pulse intensity and phase to its FROG trace. In previous work, we showed that a neural network can retrieve simple pulses rapidly and directly. This original approach involved feature extraction by computing the lowest-order integral moments of the FROG trace, making it particularly sensitive to the presence of additive noise. Using parallel-processing hardware, we are now able to use FROG traces of limited size (32 X 32 pixel) without any feature extraction as input for a neural net. In addition, FROG traces of 64 X 64 pixel size, typical for experimental data, can be used in conjunction with a more noise-insensitive feature extraction method.

Paper Details

Date Published: 22 March 1996
PDF: 7 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235946
Show Author Affiliations
Marco A. Krumbuegel, Sandia National Labs. (United States)
Kenneth W. DeLong, Sandia National Labs. (United States)
David N. Fittinghoff, Sandia National Labs. (United States)
John N. Sweetser, Sandia National Labs. (United States)
Rick P. Trebino, Sandia National Labs. (United States)

Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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