Deep learning for laser beam imprinting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F23%3A00583382" target="_blank" >RIV/61389021:_____/23:00583382 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68378271:_____/23:00573225 RIV/68407700:21230/23:00366750 RIV/00216208:11320/23:10468368

Výsledek na webu

<a href="https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-12-19703&id=531063" target="_blank" >https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-12-19703&id=531063</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1364/OE.481776" target="_blank" >10.1364/OE.481776</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning for laser beam imprinting

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep learning for laser beam imprinting

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10306 - Optics (including laser optics and quantum optics)

Projekt

<a href="/cs/project/GA20-08452S" target="_blank" >GA20-08452S: Směrem k AbloCAMu: základní přístupy k automatizaci ablačního-desorpčního imprintování fokusovaných svazků rentgenových laserů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Optics Express

ISSN

1094-4087

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

19703-19721

Kód UT WoS článku

001026189200003

EID výsledku v databázi Scopus

2-s2.0-85163592913