Real-time reconstruction of intense, ultrafast laser pulses using deep learning
Matthew Stanfield, Jordan Ott, Christopher Gardner, Nicholas F. Beier,, Deano Farinella, Christopher A. Mancuso, Pierre Baldi, and Franklin Dollar

TL;DR
This paper presents a deep learning method for real-time reconstruction of the spectral phase and temporal profile of intense, ultrafast laser pulses using nonlinear optical effects and spectral measurements, enabling single-shot, in-situ diagnostics.
Contribution
The authors introduce a novel deep learning approach that reconstructs spectral phase and fluence of ultrafast laser pulses from spectral response data, incorporating nonlinear Schrödinger equation simulations.
Findings
Accurate phase reconstruction with low error rates.
Effective in noisy conditions and with large bandwidth pulses.
Real-time, single-shot measurement capability.
Abstract
Ultrafast lasers ( fs) have enabled laser-matter interactions at intensities exceeding with only millijoules of laser energy. However, as pulse durations become shorter, larger spectral bandwidths are required. Increasing the bandwidth causes the temporal structure to be increasingly sensitive to spectral phase, yet measuring the spectral phase of a laser pulse is nontrivial. While direct measurements of the spectral phase cannot be done using square-integrable detectors, phase information can be reconstructed by measuring the spectral response of a nonlinear optical effect. We introduce a new deep learning approach using the generalized nonlinear Schr\"{o}dinger equation and self-phase modulation, a nonlinearity occurring from material propagation. By training a neural network on numerical simulations of pulses propagating in a known material,…
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Taxonomy
TopicsLaser-Matter Interactions and Applications · Laser-Plasma Interactions and Diagnostics · Advanced Fiber Laser Technologies
