Real-time reconstruction of high energy, ultrafast laser pulses using deep learning

We report a method for the phase reconstruction of an ultrashort laser pulse based on the deep learning of the nonlinear spectral changes induce by self-phase modulation. The neural networks were trained on simulated pulses with random initial phases and spectra, with pulse durations between 8.5 and 65 fs. The reconstruction is valid with moderate spectral resolution, and is robust to noise. The method was validated on experimental data produced from an ultrafast laser system, where near real-time phase reconstructions were performed. This method can be used in systems with known linear and nonlinear responses, even when the fluence is not known, making this method ideal for difficult to measure beams such as the high energy, large aperture beams produced in petawatt systems.

Automated summary

We introduce a method for phase reconstruction of ultrashort laser pulses using deep learning based on the nonlinear spectral changes caused by self-phase modulation. The method has been validated on simulated and experimental data, demonstrating its effectiveness in reconstructing the pulse phase.