Using machine-learning methods for analysing the results of numerical simulation of laser-plasma acceleration of electrons

Using machine-learning methods based on self-organising Kohonen maps, the results of numerical simulation of the acceleration of electrons during the interaction of high-power laser radiation with plasma are analysed and classified. The particle-in-cell (PIC) method is used to simulate the interacti...

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Bibliographic Details
Published in:Quantum electronics (Woodbury, N.Y.) N.Y.), 2021-09, Vol.51 (9), p.854-860
Main Authors: Volkova, T.M., Nerush, E.N., Kostyukov, I.Yu
Format: Article
Language:English
Subjects:
Electrons
High power lasers
laser plasma
Lasers
Machine learning
machine-learning methods
neural networks
Parameters
Particle in cell technique
particle-incell numerical simulation
Plasma acceleration
plasma acceleration methods
self-organising Kohonen maps
Simulation
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Summary:Using machine-learning methods based on self-organising Kohonen maps, the results of numerical simulation of the acceleration of electrons during the interaction of high-power laser radiation with plasma are analysed and classified. The particle-in-cell (PIC) method is used to simulate the interaction in a wide range of parameters (laser intensity and plasma concentration). For each set of parameters, the spectrum of accelerated electrons is found, based on which the charge, average energy, and relative energy spread of accelerated electrons are calculated. Using the obtained values as input parameters of the map, the classification of various acceleration regimes is performed. The developed scheme can be used to identify the optimal acceleration regimes under more realistic conditions, considering a larger number of parameters.
ISSN:1063-7818
1468-4799
DOI:10.1070/QEL17608