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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Optimization of laser-driven quantum beam generation and the applications with artificial intelligence
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10300 - Physical sciences

Project
<a href="/en/project/LM2018097" target="_blank" >LM2018097: R&D centre for plasma and nanotechnology surface modifications</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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