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3 October 2024 Custom deep learning enables ultrafast pulse reconstruction using Fourier transformed collinear frequency resolved optical gating
Hans D. Hallen, Josh Noble, Chen Zhou, Bill Murray, Zhiwen Liu
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Proceedings Volume 13139, Ultrafast Nonlinear Imaging and Spectroscopy XII; 131390U (2024) https://doi.org/10.1117/12.3028511
Event: Optical Engineering + Applications, 2024, San Diego, California, United States
Abstract
Full temporal characterization of optical pulses is critical in understanding ultrafast phenomena including electronic transitions, laser physics, and others. We demonstrate a machine learning-based reconstruction technique to recover the complex field from frequency resolved optically gated (FROG) interferometric data collected in the collinear acquisition geometry. FROG, or the collinear version CFROG data are time-consuming to acquire, so training a high-performance ML network with measured data is challenging. The use of simulated data for training requires careful dataset construction, as we show here. We find that the combination of using the Fourier transform instead of the raw data as an input and accurate noise modeling for the synthetic-data based training are required for a robust and accurate deep-learning-based quantification. The result is a significantly faster computation than traditional methods for inverting the CFROG experimental data, potentially enabling CFROG imaging.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hans D. Hallen, Josh Noble, Chen Zhou, Bill Murray, and Zhiwen Liu "Custom deep learning enables ultrafast pulse reconstruction using Fourier transformed collinear frequency resolved optical gating", Proc. SPIE 13139, Ultrafast Nonlinear Imaging and Spectroscopy XII, 131390U (3 October 2024); https://doi.org/10.1117/12.3028511
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KEYWORDS
Ultrafast phenomena
Deep learning
Laser frequency
Data acquisition
Education and training
Fourier transforms
Laser interferometry
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