Deep learning for laser beam imprinting.

Area of Science:

• Physics
• Materials Science
• Computer Science

Background:

• Ablation imprinting is crucial for characterizing focused X-ray laser beams, offering high dynamic range and resolution.
• Detailed beam profile analysis is vital for high-energy-density physics and nonlinear phenomena research.
• Current methods are labor-intensive due to the large number of imprints required for complex experiments.

Purpose of the Study:

• To introduce deep learning-assisted ablation imprinting for automated X-ray laser beam characterization.
• To develop and validate a convolutional neural network (U-Net) for analyzing ablation imprints.
• To reduce the manual workload and accelerate data processing in laser-matter interaction experiments.

Main Methods:

• A multi-layer convolutional neural network (U-Net) was trained on a dataset of manually annotated ablation imprints.
• The U-Net model was applied to characterize focused X-ray laser beams from beamline FL24/FLASH2 at the Free-electron laser in Hamburg.
• The neural network's performance was benchmarked against experienced human analysts.

Main Results:

• The deep learning approach successfully characterized focused X-ray laser beam profiles.
• The automated analysis demonstrated comparable performance to human experts.
• The study validates the feasibility of using AI for rapid processing of ablation imprint data.

Conclusions:

• Deep learning-based ablation imprinting offers an efficient and automated solution for X-ray laser beam characterization.
• This approach significantly reduces analysis time and human effort in complex experimental setups.
• The developed methods pave the way for a "virtual analyst" capable of end-to-end experimental data processing.