Demonstration of machine-learning-based control for femtosecond-level pulse-shaping applications

Advancements in laser-based secondary radiation sources are being made by controlling the pulse shape of ultrafast, high-power laser systems away from its best-compressed level (shortest duration). Pulse shaping at the femtosecond level relies on control over a combination of both the laser spectral intensity and phase. Devices such as an acousto-optic programmable dispersive filter are often used at high power (>1 TW) laser facilities to affect the spectral phase while largely preserving the spectral intensity and overall laser power. Using this type of controllable hardware, multiple machine-learning-based models were trained on experimental data. This was performed to capture all nonlinearities in the amplification process and to predict upstream laser input parameters needed to produce a specific output pulse shape at TW-level peak powers. Comparisons of algorithm performance are given for a variety of models and training dataset sizes, along with the results of experiments demonstrating the ability to produce a custom-defined pulse shape using dispersion-coefficient control, the first step toward arbitrary pulse-shaping control at the femtosecond level for high-power laser applications.