Investigation of electron beam effects on L shell Mo plasma produced by a compact LC generator using pattern recognition

TL;DR

This study investigates how electron beams influence L-shell molybdenum plasma spectra produced by an X pinch, using principal component analysis and neural networks to analyze polarization and plasma parameters.

Contribution

It introduces a PCA-based neural network approach to estimate plasma parameters from spectra, highlighting the effects of electron beams on plasma polarization and structure.

Findings

Electron beams induce quantized cluster formations in PCA space.

PCA combined with ANN accurately estimates plasma temperature and density.

Electron beam fraction around 0.1 influences plasma polarization characteristics.

Abstract

In this paper, the effects of electron beam on the X pinch produced spectra of L-shell Mo plasma have been investigated by principal component analysis (PCA), and this analysis is compared with that of line ratio diagnostics. Spectral database for PCA extraction was arranged using the non-LTE collisional radiative L-shell Mo model. PC vector spectra of L shell Mo including F, Ne, Na and Mg- like transitions were studied to investigate the polarization types of these transitions. PC1 vector spectra of F, Ne, Na and Mg- like transitions resulted in linear polarization of Stokes Q profiles. Besides, PC2 vector spectra showed linear polarization of Stokes U profiles of 2p53s of Ne like transitions which were recognized as responsive to the magnetic field (Trabert et al., 2017). 3D representation of PCA coefficients demonstrated that addition of electron beam to the non-LTE model generates the quantized collective clusters which are translations of each other and follow V-shaped cascade trajectories except for the case f = 0.0. The extracted principal coefficients were used as a database for the Artifical Neural Network (ANN) to estimate the plasma electron temperature, density and beam fractions of time integrated spatially resolved L-shell Mo X-pinch plasma spectrum. PCA based ANN provides advantage in reducing the network topology with a more efficient Backpropagation supervised learning algorithm. The modeled plasma electron temperature is about Te~660 eV and density ne=1x1020 cm-3 in the presence of the fraction of the beams with f~0.1 and centered energy of 5 keV.