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Deep learning for laser beam imprinting
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DOI：10.1364/OE.481776.png)
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Methods of ablation imprints in solid targets are widely used to characterize focused X-ray laser beams due to a remarkable dynamic range and resolving power. A detailed description of intense beam profiles is especially important in high-energy-density physics aiming at nonlinear phenomena. Complex interaction experiments require an enormous number of imprints to be created under all desired conditions making the analysis demanding and requiring a huge amount of human work. Here, for the first time, we present ablation imprinting methods assisted by deep learning approaches. Employing a multi-layer convolutional neural network (U-Net) trained on thousands of manually annotated ablation imprints in poly(methyl methacrylate), we characterize a focused beam of beamline FL24/FLASH2 at the Free-electron laser in Hamburg. The performance of the neural network is subject to a thorough benchmark test and comparison with experienced human analysts. Methods presented in this Paper pave the way towards a virtual analyst automatically processing experimental data from start to end.
Keyword:
WAVE-FRONT
RAY
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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引用论文
In situ single-shot diffractive fluence mapping for X-ray free-electron laser pulses.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Non-thermal desorption/ablation of molecular solids induced by ultra-short soft x-ray pulsesOPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
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