Počet záznamů: 1
Deep learning for laser beam imprinting
- 1.0583382 - ÚFP 2024 RIV US eng J - Článek v odborném periodiku
Chalupský, J. - Vozda, V. - Hering, J. - Kybic, J. - Burian, Tomáš - Dziarzhytski, S. - Frantálová, K. - Hájková, V. - Jelínek, Šimon - Juha, L. - Keitel, B. - Kuglerová, M. - Kuhlmann, M. - Petryshak, B. - Ruiz-Lopez, M. - Vyšín, L. - Wodzinski, T. - Plönjes, E.
Deep learning for laser beam imprinting.
Optics Express. Roč. 31, č. 12 (2023), s. 19703-19721. ISSN 1094-4087
Grant CEP: GA ČR(CZ) GA20-08452S
GRANT EU: European Commission(XE) 654148 - LASERLAB-EUROPE
Institucionální podpora: RVO:61389021
Klíčová slova: deep learning * laser beam imprinting * X-ray
Obor OECD: Optics (including laser optics and quantum optics)
Impakt faktor: 3.8, rok: 2022
Způsob publikování: Open access
https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-12-19703&id=531063
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.
Trvalý link: https://hdl.handle.net/11104/0351354
Počet záznamů: 1