Optimization of laser-driven quantum beam generation and the applications with artificial intelligence

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F24%3A00136155" target="_blank" >RIV/00216224:14310/24:00136155 - isvavai.cz</a>

Výsledek na webu

<a href="https://pubs.aip.org/aip/pop/article/31/5/053108/3295205/Optimization-of-laser-driven-quantum-beam" target="_blank" >https://pubs.aip.org/aip/pop/article/31/5/053108/3295205/Optimization-of-laser-driven-quantum-beam</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0190062" target="_blank" >10.1063/5.0190062</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of laser-driven quantum beam generation and the applications with artificial intelligence

Popis výsledku v původním jazyce

We have investigated space and astrophysical phenomena in nonrelativistic laboratory plasmas with long high-power lasers, such as collisionless shocks and magnetic reconnections, and have been exploring relativistic regimes with intense short pulse lasers, such as energetic ion acceleration using large-area suspended graphene. Increasing the intensity and repetition rate of the intense lasers, we have to handle large amounts of data from the experiments as well as the control parameters of laser beamlines. Artificial intelligence (AI) such as machine learning and neural networks may play essential roles in optimizing the laser and target conditions for efficient laser ion acceleration. Implementing AI into the laser system in mind, as the first step, we are introducing machine learning in ion etch pit analyses detected on plastic nuclear track detectors. Convolutional neural networks allow us to analyze big ion etch pit data with high precision and recall. We introduce one of the applications of laser-driven ion beams using AI to reconstruct vector electric and magnetic fields in laser-produced turbulent plasmas in three dimensions.

Název v anglickém jazyce

Optimization of laser-driven quantum beam generation and the applications with artificial intelligence

Popis výsledku anglicky

We have investigated space and astrophysical phenomena in nonrelativistic laboratory plasmas with long high-power lasers, such as collisionless shocks and magnetic reconnections, and have been exploring relativistic regimes with intense short pulse lasers, such as energetic ion acceleration using large-area suspended graphene. Increasing the intensity and repetition rate of the intense lasers, we have to handle large amounts of data from the experiments as well as the control parameters of laser beamlines. Artificial intelligence (AI) such as machine learning and neural networks may play essential roles in optimizing the laser and target conditions for efficient laser ion acceleration. Implementing AI into the laser system in mind, as the first step, we are introducing machine learning in ion etch pit analyses detected on plastic nuclear track detectors. Convolutional neural networks allow us to analyze big ion etch pit data with high precision and recall. We introduce one of the applications of laser-driven ion beams using AI to reconstruct vector electric and magnetic fields in laser-produced turbulent plasmas in three dimensions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10300 - Physical sciences

Projekt

<a href="/cs/project/LM2018097" target="_blank" >LM2018097: Centrum výzkumu a vývoje plazmatu a nanotechnologických povrchových úprav</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Physics of Plasmas

ISSN

1070-664X

e-ISSN

1089-7674

Svazek periodika

31

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

001233619700003

EID výsledku v databázi Scopus

2-s2.0-85194965403